1

Program

Wednesday, December 6, 2006 7:00 – 8:00 pm NSIF Board of Directors Meeting – Embassy Suites Thursday, December 7, 2006 8:00 – 8:30 am Registration

8:30 – 10:00 Genetic Improvement in Poultry & Breeding Herd Productivity Views from The Poultry Breeding Industry - Dr. David Pollock - Perdue Poultry

Value of Pig Quality vs. Pig Quantity at Weaning - Dr. Justin Holl – Smithfield Premium Genetics

Break 10:00 – 10:30

10:30 – 12:00 Genetic Aspects and Technologies to Improve Reproduction Factors affecting litter size in pigs- Dr. Jeff Vallet - USDA – MARC Increasing sperm production in mature boars via manipulation of their neonatal environment - Dr. Billy Flowers – North Carolina State University Cryo-preservation of Semen and Embryos - Dr. Eric Walters – University of Missouri

12:00 – 1:30 pm NSIF Awards Luncheon and Annual Meeting

1:30 – 3:30 New Research on Production and Behavior Traits

Selection for decreased Residual Feed Intake (RFI) in Yorkshire swine - Dr. Jack Dekkers and Weiguo Cai - Iowa State University Comparison of 1980’s and Modern Swine Genetic Types

Growth, Feed Intake, Feed Efficiency - Dr. Todd See – NC State University Carcass and Pork Quality - Justin Fix – North Carolina State University Behavior and Structure - Dr. Todd See – North Carolina State University

Break 3:00 pm – 3:30 pm

3:30 – 5:00 Board of Directors Round-table Discussion

NSIF - Defining our Future! - Discussion to address key elements of NSIF: Focal points to include: Membership and Participation, Annual Meeting and Symposia Focus, Role of NSIF in the North American Genetics Industry, Target Audiences for the Future.

Todd See, Moderator; Dale Miller, Everett Forkner, Fields Gunsett, Tom Baas, Scott Newman, Mark Boggess, Harold Hodson

<cont>

2

3

Friday, December 8, 2006

8:30 – 10:00 Swine Genome Update and Breeding Herd Productivity Sequencing the Swine Genome: Progress and Prospects - Dr. Max Rothschild – ISU Candidate Genes Associated With Sow Longevity - Benny Mote – Iowa State University Factors Influencing Sow Culling - Mark Knauer – North Carolina State University

Break 10:00 – 10:30am

10:30 – 12:00 Behavior and Genetic Improvement Strategies Feeding Behavior and its Impact on Economically Important Traits - Dr. Joe Cassady – North Carolina State University

Managing selection and diversity in a breeding program - Dr. Scott Newman - PIC

National Pork Board Research and Committee Update - Dr. Mark Boggess – National

Pork Board Update on Progress toward a New NSIF Logo - Dale Miller – National Hog Farmer

Magazine

12:00 – 1:00 Lunch – Sandwich Buffet for All NSIF Board of Directors Meeting- Board Members

4

5

2006 National Swine Improvement Federation Swine Genetics Symposium Sponsors

Babcock Genetics, Inc. P.O. Box 759, Rochester, MN 55903 Phone (507) 288-3312 or (800) 343-4940: Fax (507) 288-2078: www.babcockgenetics.com Genetic Improvement Services of North Carolina, Inc. PO Box 9, 6980 Harper House Rd., Newton Grove, NC 28366-0009:Phone (910) 594-2353: Fax (910) 594-0238: www.gis-swine.com/ Hermitage NGT. P.O. Box 129, Columbus, Nebraska 68602-0129 Phone (402) 564-2707: Fax (402) 563-3847: www.hermitagengt.com Iowa Pork Industry Center. Iowa State University, 109 Kildee Hall, Ames, IA 50011 Phone (515) 294-4103: Fax (515) 294-5698: www.ipic.iastate.edu Monsanto Choice Genetics. 800 N. Lindberg, B2NA, St. Louis, MO 63167 Phone (866) 237-8744: www.monsantochoicegenetics.com National Swine Registry. P.O. Box 2417, West Lafayette, IN 47996 Phone (765) 463-3594: Fax (765) 497-2959. www.nationalswine.com Newsham Genetics. 5058 Grand Ridge Drive, Suite 200, West Des Moines, IA 50265 Phone (800) 622-2627 or 515-225-9420: Fax (515) 225-9560: www.newsham.com PIC USA. 100 Bluegrass Commons Boulevard, Suite 2200, Hendersonville, TN 37075 USA Phone (800) 325-3398: Fax (615) 265-2844: www.pic.com S&S Programming, Inc. Keith Schuman, Duncan Innovation Center, 3601 Sagamore Pkwy. N. Suite F, Lafayette, IN 47904. (765) 423-4472: 1.877.688.1589 National Swine Improvement Federation. 122 An Sci Bldg, 2029 Fyffe Ct, Columbus, OH 43210 Phone (614) 688-3686: Fax (614) 292-3513: www.nsif.com

6

7

Views from the Poultry Breeding Industry

D.L.Pollock, PhD

Director of Genetics, Heritage Breeders,

10789 Stewart Neck Rd., Princess Anne, MD 21853

Introduction Over the past 50 years the broiler industry has grown significantly in the US as the demand for broilers and broiler meat grew at around 5% annually to the current 90+ lbs/ capita. The pattern of consumption has changed as consumer preference is for ready to heat or ready to eat products. Whole bird sales are now less than 8%. Traditional hamburger fast-food outlets now sell more chicken products than beef. Healthy, low fat, value for money, flexible, choice of products, and no religious bias, are all part of the success of the broiler business. But none of this would have taken place without the biological ability to provide the numbers of broiler chicks necessary, the feed needed to fuel growth, and the continuously improving growing and processing facilities. Consolidation of processors( Tyson, Pilgrim and Perdue control 60%) has led to consolidation of Primary Breeder companies. Currently there are 4 broiler primary breeders supplying 98% of the US and 80% of the world. Egg layer primary breeders and turkeys are in a similar situation. Table 1. Poultry companies and Products Company Owner Products US % Aviagen Wesjohann Ross,AA,

IR broilers

Hyline Egg layers

Nicholas Turkeys

BUT Turkeys

Hyvac 30:70

CVI Tyson Cobb, Vantress broilers

50:20

Hubbard Grimaud Hubbard broilers

10:1

Heritage Perdue Heritage broilers

9:8

Pureline ? Pureline broilers

?

Case Case ? Hybro Hybrid ? Gallus Gallus domesticus advantages There are some species advantages shown by broilers that are not demonstrated at all or at a lesser level in quadrupeds that have impacted genetic improvement both directly and indirectly.

• Large effective population sizes, Ns = 180 – 360 depending on line and multiplication supply

needs • One sire pedigree pen is 6’x8’ • Fecundity- one female can produce 40 to 50 progeny to 45 weeks age • Generation intervals are between 35 and 50 weeks of age depending on role • The need to include egg production extends the female line intervals • Eggs- stratification and disease control • G/E evaluations • Phenotype manipulation- broilerization, restriction of mature size • Magnitude of heterosis- up to 30% in some 2 way crosses

8

• Magnitude of specialization – think of egg-layers vs broilers o Egg-layers laying 266 eggs in 280 days but will only weigh 2 lbs at 50 days o Broilers weighing 5.75 lbs at 50 days but only laying 110 eggs in 280 days (150 eggs with

modified phenotypes). o And within broiler lines Table 2 demonstrates the range in the 4 main lines in the

Heritage program. Table 2: Heritage Pureline and Crossbred Performance

Grow-out to 60 days Reproductive Performance

25 to 60 weeks

Line Livability %

Livewt afcr % yield

%dbm/

livewt

HEP Hatch %

chix Usable chix

Female Parent

Equivalents

Bro male

87.9 9.11 lb 68.9g/

d

2.024 72 23.41 85 65 55 23 9.13 mil

Roa male

90.2 8.36 lb 63.3g/

d

1.950 74 26.27 80 60 49 21 11.16 mil

Roa fem

91.3 7.38 lb 55.8g/

d

2.134 71.7 24.25 120 78 94 38 6.56 mil

Roa fem

91.7 7.85 lb 59.4g/

d

2.092 72 24.04 110 75 82 35 7.12 mil

PS cross

92.9 8.14 lb 61.6g/

d

2.020 71.9 24.06 145 82 118 116 4.75 mil

Roa cross

96.5 8.00 lb 60.5g/

d

1.940 72 23.5

Specialization It is possible to group lines by function, male or sire lines and female or dam lines. In the Heritage program our 2 male lines are significantly faster growing (52/53 days to 8.00 lbs for the broiler line and 57/58 days for the roaster line.) Egg production is not recorded nor included in the selection process so all intensity goes to growth, yield and fcr(residual feed consumption). If egg production is decreasing over time then we will simply place more GP flocks. The only commercially important reproductive trait is fertility at the PS level and manipulation of the phenotypes containing any correlated genetic trends. Chick production in these male lines( 28-60 weeks of age) averages 49 to 55 cph. Provided the growth traits are improving, egg production will not become critical until selection intensities are reduced. In the female lines reproductive traits are 30-40 eggs per hen higher or about 40-50% more. Livability is better but growth rate is slower and feed conversion rate is poorer. Historically about 50% of the selection intensity went to egg production and 50% to growth and yield. Selection for fcr(residual feed consumption) was not included until 4 years ago after we reached our egg production target of 160. Going forward we expect the differences between the male lines and female lines to broaden since generally speaking heritabilities are maintained at much the same rate as before. We are moving to a continuous or overlapping generation procedure with 9 mating units regenerating annually( 54 weeks ). This suits our Salmonella eradication initiative and spreads the program to minimize risk.

9

The picture in the other broiler primary breeders is similar. This magnitude of performance difference does not appear to exist in the swine breeding world between sire and dam lines. Heterosis Heterosis is the performance difference observed between the cross and the parental lines. This can be either positive or negative. It is the property of a specific cross and is predictable. Heterosis can provide a large payback to a breeding program. Our Heritage parent stock female was originally identified in a series of di-allele cross tests. In the 1980s our roaster product was a 2 way cross but as demand grew the female line began to be limiting in terms of chick production. The two strategies applied were to increase selection pressure in egg production and look for a 2 way cross female. A candidate cross was identified out of a 6x6 design. This cross demonstrated higher than expected performance. The cross was further evaluated and when the performance was repeated, plans were made to supply 1.5 million breeder females as quickly as possible. Table 2 shows the heterotic advantage to both growth and reproductive traits. The advantage in chick production is due to the heterotic effects on livability, fertility and hatchability as well as rate of lay. We have tested the reciprocal mating and observed a reduction of five chicks but a marginal gain on growth and carcass quality. If the company accepts this bird we could produce 2 hybrids and gain efficiencies in the use of off-sex chicks. It is doubtful if di-allele testing is normal or routine in swine operations. I remember listening to Maurice Bichard in the early days of PIC (ca. 1975) and it seemed to me that they were largely focused on pureline improvement. Maybe genomics will allow measurements of genetic distance to be a way to increase hybrid vigor. Normally it is considered that heterotic effects are observed in traits with low heritabilities. We have measured heritabilities of 0.30 in egg production in broiler female lines while the literature describes many estimates of .05-.10 in egg-layers. Growth rate is considered moderate to highly heritable ( 0.20-0.30) but still there is heterosis to be obtained. Interestingly, broiler flock livabilities are consistently 95-96%, largely due to heterosis. Heterotic effects are present in swine but they are expensive to quantify. Generally the annual gains observed with broilers are around 0.5 to 0.75 days reduction to kill weight. The industry seems to be moving in 2 directions with approximately equal volumes. There is the traditional small bird( 4.00 lb) for fast food outlets and newer big bird deboners (7.0-8.0 lbs). The Cobb 500 and the Ross308 are more suited for the small bird program while the Ross708, Ross UY2, and the Heritage breeds are for deboning. The Future Currently the industry is cutting back because the market is significantly oversupplied and feed prices are predicted to increase. In 1984 the economic weightings for breast meat were $2.50/lb;in 2006 it is $1.00/lb. The US is still a white meat market. Consolidation will continue and perhaps more importation will occur if security of supply can be guaranteed. Heritabilities and heterosis of the main traits remain at the level of 30 years ago and genomics through market assisted selection will be a way to improve secondary traits (eg egg production in male lines). Although live weights have increased significantly, inherent biological limits will prevent broilers from becoming turkeys. Our heaviest male line males can grow to 18 lbs at maturity so that is probably the limit. Growth rate has increased dramatically and it is possible to predict that the Perdue roaster which currently reaches 8.00 lbs live weight at 59 days will in 75 (a geneticist’s lifetime) years from now reach 8.00 lbs in 1 day!

10

11

Improving Weaned Pig Quality in Today’s Large Litters J. Holl and T. Long

Smithfield Premium Genetics Group, Rose Hill, NC.

Introduction Reproductive efficiency is an important component of profitable pig production. Litter size has been used in the swine breeding industry as a major indicator of reproductive efficiency. As a result, litter size has increased and continues to be a primary objective in most maternal-line selection programs. With increases in litter size, total litter weaning weight has also increased (Figure 1.). Total weaning weight is a function of pig quantity and quality. Pig quality can be associated with individual pig weights and survivability. As litter size increases, total weaning weight increases at a decreasing rate.

As shown in Figure 1., larger litter sizes not only wean more weight, but also have higher preweaning mortality. Increased litter size has been associated with smaller, more fragile piglets. Therefore, the system-wide impact of increased litter size could be limited due to production losses and inefficiencies associated with decreased pig quality. In order to maximize the economic impact of increased genetic potential for litter size, pig quality should be addressed and incorporated into selection programs.

Materials and Methods Data were recorded from pureline Large White animals at SPG nucleus farms. Farms were negative for porcine reproductive and respiratory syndrome virus (PRRSv), Actinobacillus pleuropneumonia (APP), and Mycoplasma hyopneumonia (Myco). At birth, numbers of stillborn and live born pigs were recorded along with individual birth weight. Cross-fostering was done within 24 hr of birth. Individual mortality events and dates were recorded on each pig. For this analysis, pre-weaning mortality was defined as a stillborn pig or a pig that died before weaning. At weaning (17 to 25 d), individual weaning weight and nurse dam were recorded for each pig. A portion of the pigs was subsequently finished with serial feed intake data recorded using Osborne FIRE feeders. At approximately 170 d, pigs were weighed (WT), and ultrasound measurements for backfat (BF), loineye depth (LD), and intramuscular fat (IMF) were recorded. A threshold-linear model was used to analyze preweaning mortality and birth weight. The model included fixed effects of dam parity, birth litter size, and contemporary group. Contemporary group was defined as piglets born on the same farm, year, and month. Piglet gender was used as a fixed effect for birth weight only. Random effects included direct and maternal additive genetic, litter, and residual effects. For preweaning mortality, nurse dam was fit for the maternal genetic and litter effects.

Figure 1. Total Weaning Weight and Preweaning Mortality by Litter Size

0.0

5.0

10.0

15.0

20.0

<= 7 8 9 10 11 12 13 14 15 >=16

Litter Size (NBA)

PWM

(%)

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

Litte

r WW

T (lb

s)

PWMWWT

12

Regression analyses were done to determine birth and weaning weight impacts on subsequent performance. Regression of birth and weaning traits on off-test traits (WT, AFI, BF, LD, and IMF) included fixed effects of contemporary group, gender, and a covariate of age. Contemporary group was defined as piglets weighed on the same farm, year, and week.

Results and Discussion When evaluating phenotypic associations, an indirect relationship existed between litter size and average birth weight (Figure 2). As litter size increased, birth weight decreased. More specifically, the percentage of pigs that were born weighing less than 2 lbs increased. A greater percentage of lighter pigs died during the preweaning phase compared to heavier piglets. For pigs less than 1.5 lbs, only 47% survived to weaning. For pigs between 1.5 and 2.0 lbs, 64% survived to weaning. Pigs weighing between 2.0 and 3.0 lbs had about 85% survival, and pigs weighing over 3.0 lbs at birth had a 95% survival rate. Therefore, processes to increase average birth weight in larger litters may reduce preweaning mortality. Estimates of heritabilities and genetic correlations between birth weight and preweaning mortality from the joint threshold-linear model are shown in Table 1. Maternal heritabilities were greater than direct genetic heritabilities. A greater opportunity may exist to place selection pressure on improving the sow’s

ability to successfully raise a litter rather than selecting on the direct genetic effects. Genetic correlations between birth weight and preweaning mortality were negative for direct and maternal components. These estimates were in agreement with estimates reported by Damgaard et al. (2003) and Hogberg and Rydhmer (2000). Thus, a pig’s genetic predisposition for increased birth weight would also correlate to a greater ability to survive. In addition, a sow’s genetic predisposition for producing heavier piglets at birth was correlated with a genetic predisposition for weaning a greater proportion of piglets. Consequently, lighter piglets would be more apt to die before weaning. However, selection to decrease preweaning mortality may increase birth weights. Table 1. Heritabilities and genetic correlations for direct (d) and maternal (m) genetic components for birth weight (BW) and preweaning mortality (PWM) BWd BWm PWMd PWMm

BWd 0.04 -0.24 -0.34 -0.09 BWm 0.15 0.12 -0.16

PWMd 0.03 -0.66 PWMm 0.09

Figure 2. Birthweight by Litter Size

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

7 8 9 10 11 12 13 14 15 16

Litter Size (NBA)

Avg

BW

and

%<2

lb

Avg Birth Wt% <2lbs

13

Results from regression of birth and weaning traits on off-test production traits are shown in Table 2. Increased birth and weaning weights were associated with faster postweaning growth, but were not associated with greater daily feed intake. Increased birth and weaning weights were also associated with less fat, muscle depth, and intramuscular fat. However, the magnitude of effects, although significant, is relatively small considering the amount of variation in early age pig weights. Assuming a weaning weight of 12 lbs, 190 days to 250 pounds, and 0.70 inches backfat, an increase of 1 lb or 8.3% in weaning weight would result in a decrease of 1.5 days to market and 0.02 inches reduction in backfat. These phenotypic relationships indicate that increasing birth and/or weaning weights may have some beneficial impacts on finishing performance and carcass characteristics.

Table 2. Regression of birth weight(BW), weaning (WWT) weight and weaning age on Days to 250 lbs, Test weight, average daily feed intake (AFI), ultrasound backfat (BF), ultrasound loin depth (LM), and

ultrasound intramuscular fat percentage (IMF). BW WWT Wean Age Trait Estimatea Estimatea Estimateb

Days to 250 -0.62 ± 0.01 ** -0.15 ± 0.01 ** 0.01 ± 0.01 NS

Test Wt (lbs) 1.27 ± 0.03 ** 0.32 ± 0.01 ** -0.02 ± 0.02 NS

AFI (lbs) 0.0014 ± 0.0022 NS 0.0002 ± 0.0006 NS -0.0007 ± 0.0012 NS

BF (in) -0.0023 ± 0.0002 ** -0.0003 ± 0.0001 ** -0.0003 ± 0.0001 **

LM (in) -0.0007 ± 0.0003 ** -0.0003 ± 0.0001 ** -0.0001 ± 0.0001 NS

IMF -0.0050 ± 0.0009 ** -0.0008 ± 0.0003 ** -0.0003 ± 0.0005 NS aEffect per one-tenth pound increase in wt bEffect per one day increase in wean age

One method to increase weaning weights is to increase weaning age. Although weaning age was positively associated with weaning weight, weaning age did not have as many significant associations with growth and carcass traits. This was in contrast to a report by Main et al. (2004). However, the study herein looked at an older range in weaning age compared to the other report. Therefore, using management to change weaning weight by changing weaning age may not have a desired impact on down-stream growth performance. Other research has reported that heavier pigs at weaning are more likely to survive in the post-weaning phase (Larriestra et al. 2006 and de Grau et al. 2005). Using management practices such as increasing weaning age to increase weaning weight may help decrease post-weaning mortality. In addition, genetic relationships between weaning weight and post-weaning mortality need to be evaluated for possible use in selection objectives.

Conclusions Selection has effectively increased the number of pigs born alive. However, increased litter size has been associated with reduced pig quality in terms of smaller piglets and higher preweaning mortality. In order to fully capture the potential impact of litter size selection, selection should include components of pig quality.

References Damgaard, L.H., Rydhmer, L., Lovendahl, L. and Grandison, K. 2003. J. Anim. Sci. 81:604-610.

de Grau, A., Dewey, C., Friendship, R. and de Lange, K. 2005. Can. J. Vet. Res. 69:241-245.

Hogberg, A. and Rydhmer, L. 2000. Acta Agric. Scand. Sect. A. 50:300-303.

14

Larriestra, A.J., Wattanaphansak, S., Neumann, E.J., Bradford, J., Morrison, R.B. and Deen, J. 2006. Can. Vet. J. 47:560-566.

Main, R.G., Dritz, S.S., Tokach, M.D., Goodband, R.D. and Nelssen, J.L. 2004. J. Anim. Sci. 82:1499-1507.

Factors Affecting Litter Size in Pigs1

J. L. Vallet, B. A. Freking, J. R. Miles, J. A. Nienaber, and T. M. Brown-Brandl

USDA, Agricultural Research Service, U.S. Meat Animal Research Center,

P.O. Box 166, Clay Center, NE 68933-0166, USA 1 Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.

Litter size in pigs has been defined in a number of ways, depending on the goals of the specific experiment. Number of total piglets, including mummies and stillborns, number of fully formed piglets, excluding mummies but including stillborns, and the number of piglets born alive have all been used as endpoints of litter size research. The end measures are chosen with the idea that the causes of late stage fetal death (resulting in mummies) or factors involved in stillbirth are separate from other pregnancy related factors regulating the total number of piglets born, and should therefore be studied separately. While this strategy is useful, clearly the results generated may not be completely relevant to the endpoint desired by the swine producer, which is more marketable pigs per sow. From the producer’s perspective, preweaning loss is also relevant. Because the goal of our research is to improve the efficiency of swine production and therefore increase marketable pigs per sow, we have shifted our research focus to include research to define factors influencing preweaning survival.

The paragraph above alludes to the complexity of the interacting factors influencing the number of piglets weaned. We divided piglets weaned into prenatal factors influencing the number of fully formed fetuses at the end of pregnancy, perinatal factors influencing stillbirth and postnatal factors influencing preweaning survival. Because these divisions occur sequentially, pregnancy factors also affect subsequent stillbirth and preweaning survival, and perinatal factors also affect preweaning survival.

The number of fully formed piglets present at farrowing is influenced by the number of ova shed, the fertilization failure rate, the embryonic mortality rate, and the number of fetuses maintained by the uterus during gestation (uterine capacity; Bennett and Leymaster, 1989). Note that two of these factors are rates of occurrence. The number resulting from these processes depends on the number before the processes took place, and therefore these rates do not actually represent real limitations to the number of fully formed piglets. For example, if one wishes to increase the number of fertilized ova, and fertilization failure rate is relatively fixed (most studies indicate a failure rate of 10% or less, Polge 1978), one only needs to increase the number of ova. In contrast, the number of ova shed and uterine capacity are not rates, and the number of fully formed piglets at farrowing is related to the most limiting of these two factors (the number of ova shed must be discounted by the fertilization failure rate and embryonic mortality rate, but the number surviving these two rates of occurrence can easily be increased by increasing the number of ova shed). In today’s production setting, the number of ova shed, even after discounting for fertilization failure rate and embryonic mortality rate, usually exceeds uterine capacity, and the number of fully formed fetuses is thus primarily limited by uterine capacity (Town et al., 2005).

The term “uterine capacity” suggests that the limitation to the number of fully formed fetuses that it represents is entirely due to uterine factors, but this is not the case. Uterine capacity, or the number of fully formed fetuses that can be maintained by the uterus until farrowing, is actually the result of the interaction between uterine, placental and fetal factors influencing the survival of the fetus during pregnancy. Two primary uterine factors affecting uterine capacity are uterine blood flow and uterine protein secretion. Two primary placental factors affecting uterine capacity are the size of the placenta and the efficiency of nutrient transport. Fetal factors influencing uterine capacity include fetal erythropoiesis and fetal nutrient usage. Each factor displays more or less compensatory behavior as the number of fetuses within the uterus increases.

16

16

Studies indicate that uterine blood flow increases throughout gestation (Pere and Estienne, 2000). However, uterine blood flow appears to be maximal at around 5 fetuses per uterine horn, and flow does not increase with further increases in the number of fetuses present. Because maternal blood is the ultimate source of all nutrients for the developing fetus during pregnancy, the failure of blood flow to compensate for increased fetal numbers would seem to be a serious limiting factor. Although it has been suggested that placental estrogens regulate uterine blood flow (Geisert et al., 1990), regulation of uterine blood flow during gestation in the pig is not completely understood.

Uterine protein secretion during early pregnancy and placental size during later pregnancy would seem to be interrelated. Pig blastocysts transform from a spherical to a filamentous form around day 11 of pregnancy, and this transformation has been termed elongation (Geisert et al., 1982). It is likely that the size of the placenta is ultimately determined by the extent of blastocyst elongation during this period. Furthermore, blastocysts do not elongate past each other, and it is likely that substantial elongation of neighboring conceptuses can interfere with the elongation process. The Chinese Meishan breed of pig is reported to have greater uterine capacity (Haley and Lee, 1993), and this is likely due to the fact that both the placenta and the fetus of the Meishan are smaller (Christenson, 1993). The small placenta of the Meishan can be traced to slower growth of the Meishan blastocysts during early pregnancy, resulting in more limited elongation of the Meishan blastocyst on day 11 of pregnancy (Anderson et al., 1993). Subsequent research demonstrated that this phenomenon was primarily uterine in origin (Youngs et al., 1994), and Vallet et al., (1998) reported that this was likely due to decreased uterine protein secretion by the Meishan uterus, limiting Meishan conceptus growth and elongation. Thus it appears that one component of the improved uterine capacity of the Meishan is limitation of blastocyst elongation by the Meishan uterus, mediated in some way by reduced protein secretion, which then results in reduced negative interactions between neighboring blastocysts allowing more uniform placental sizes. We attempted to mimic the reduction of uterine protein secretion and conceptus development in European breed pigs using the progesterone inhibitor RU486, and were successful in reducing both uterine protein secretion and conceptus development on day 11 of pregnancy. However, this did not result in subsequent increased uterine capacity (Vallet and Christenson, 2004). Nevertheless, these experiments point to the importance of uterine protein secretion in controlling conceptus elongation, placental size and uterine capacity. We subsequently used proteomic technology to identify proteins secreted by the uterus during this period (Kayser et al., 2006). Two glycolipid metabolizing proteins were identified, suggesting that glycolipid metabolism may play a role in the elongation process. Glycolipids play roles in numerous biological processes (Perry and Hannun, 1998; Roviezzo et al., 2004) and we have recently begun exploring changes in the endothelial differentiation gene (EDG) receptors, which respond to byproducts of glycolipid metabolism (Ishii et al., 2004). We have found interesting temporal changes in the expression of some of them, suggesting that glycolipids may play a role in the elongation process.

Placental function also influences the health of the developing fetus, and thus contributes to uterine capacity. The most important placental function that is relevant to uterine capacity is the efficiency with which the placenta transfers nutrients. Placental efficiency has received a great deal of attention in the last decade or so, largely through the work of Steve Ford and coworkers. They introduced the concept of the fetal weight to placental weight ratio as a measure of placental efficiency (Biensen et al., 1998), and went on to report that selection for this trait led to increased litter size (Wilson et al., 1999). However, their selection experiment employed only 12 animals, which were selected for only a single generation. A more recent selection experiment using divergent selection for the fetal weight to placental weight ratio, which employed more sows and 4 generations of selection, did not result in improvements in litter size (Mesa et al., 2005). One of the primary reasons for this might be that other factors affect the weight of the fetus besides placental function, and in fact, above a moderately sized placenta, the size (and therefore the function) of the placenta has no effect on the size of the fetus (Vallet, 2000). Thus, other measures of placental efficiency are needed to begin to effectively select for this trait.

We hypothesized that beyond the actual size of the placenta, the structure of the placenta with relation to the flow of maternal and fetal blood also likely affects efficiency. Exchange by simple diffusion is governed by several factors, including whether blood flows are concurrent, crosscurrent or countercurrent, the length of time the two blood systems are closely apposed to each other, and the actual distance between the two blood systems. The maternal and fetal blood supplies of the pig occur in

17

17

a cross-countercurrent arrangement (Leiser and Dantzer, 1988) that is at right angles to the plane of the placenta and the exchange of nutrients takes place within microscopic folds of the placenta. These folds become deeper and more complex with advancing gestation, and the distance between the maternal and fetal blood supplies also decreases. Both the changes in the placental folds and the decreased distance between blood supplies play a role in the improvement of placental efficiency with advancing gestation. We have also recently shown that the depth of the folds is greater in placenta of small fetuses, suggesting that deepening of the folds may be one placental mechanism that compensates for inadequate placental size and therefore uterine space. How the microscopic folds develop is completely unknown, but they are embedded in a placental stroma that we have recently shown is partially composed of hyaluronan. Hyaluronan, hyaluronan synthesizing and degrading enzymes, and hyaluronan binding proteins play significant roles in tissue morphogenesis and angiogenesis (Laurent and Fraser, 1992; Knudson and Knudson, 1993; Spicer and Tien, 2004). Thus, hyaluronan synthesizing and degrading enzymes likely play a role in formation and vascularization of the placental folds, and we have recently demonstrated changes in the expression of two forms of hyaluronidase, 1 and 2, during gestation. We are continuing to explore their role in placental development and efficiency.

Turning to fetal function, we have performed several experiments that suggest a role for the development of the blood supply in uterine capacity and litter size. Our experiments have demonstrated (1) that fetal hematocrits are impaired in small fetuses (Pearson et al., 1998; Vallet et al., 2002), (2) that the development of the blood supply takes place more quickly in Meishan fetuses (Pearson et al., 1998; Vallet et al., 2003), (3) that selection for increased uterine capacity is associated with increased fetal hematocrit (Vallet et al., 2001), (4) that a genetic marker based on the erythropoietin receptor (EPOR) is associated with improved uterine capacity and litter size (Vallet et al., 2005), and (5) that the EPOR marker is associated with increased fetal liver EPOR expression during the development of the fetal blood supply. We have also explored the ability of the fetus to modify the growth of various organs in response to nutrient deficiencies and selection for uterine capacity (Vallet and Freking, 2006). Our results indicate that both the brain and heart possess mechanisms that preserve their growth within the fetus, while the liver and spleen do not. Furthermore, subtle alterations in the growth of the fetal liver were associated with selection for uterine capacity. Taken together, these results confirm that mechanisms within the fetus also contribute to overall uterine capacity and litter size.

Several perinatal factors influence stillbirth, but one of the most important factors would appear to be the process of farrowing itself (Van Dijk et al., 2005). To get a better idea of interrelationships between the farrowing process and the incidence of stillbirth, we began monitoring farrowing and stillbirth in first parity gilts through the use of video surveillance. Cameras were mounted at the rear of farrowing crates and images were obtained at 1 to 5 second intervals until farrowing was completed. Videos were assessed for the birth of each piglet, and whether the piglet was born alive. Individual piglet farrowing intervals were calculated during the farrowing process. Farrowing intervals were longer at the beginning of the process and became shorter in the middle. Curiously, birth of the last piglet in the litter was significantly delayed, and the average farrowing interval for this piglet was 2.5 times that of the piglet before. Turning to stillbirth rate, in contrast to farrowing intervals, stillbirth rate became progressively greater as the farrowing process continued, increasing from about 2% at the beginning of farrowing to about 5% toward the end. Although changes in stillbirth rate did not mirror changes in farrowing interval, the stillbirth rate for the last pig in the litter was estimated to be 15%, 2.5 times that of the piglet before and similar to the difference in farrowing interval. Average farrowing intervals were longer for small litters, especially during July when temperatures were high, and decreased with increasing litter size. The decrease in farrowing interval with increasing litter size, combined with the unusually long farrowing interval of the last piglet, suggests a piglet or placental contribution to the speed of the process. Although cortisol (First and Bosc, 1979), prostaglandin and oxytocin (Gall and Day, 1987) can all initiate farrowing, very little is known of factors that facilitate farrowing in pigs, with the exception of oxytocin and relaxin (Guthrie, 1985; Wathes et al., 1989; Gilbert et al., 1994; Cho et al., 1998;). We collected blood samples on days 110 and 113 of gestation from the same gilts, to measure progesterone, estrogen and relaxin, and will attempt to relate the results to farrowing intervals. Thus far, progesterone does not appear to influence farrowing intervals.

18

18

Finally, we turn to preweaning survival. Numerous studies indicate that one of the primary factors influencing preweaning survival is low birth weight (Tuchscherer et al., 2000; Milligan et al., 2002), which brings us back to uterine capacity, because birth weights are established during gestation. Beyond birth weights, piglets from different breeds or lines of pigs differ widely in preweaning survival. Using data from our USMARC swine database, we could show that for piglets below a birth weight of 1 kg, our lean and obese selected lines differed dramatically in piglet survival. Losses for these low birth weight piglets were 4 times greater in the lean line than the obese line. Similar results were obtained comparing Meishan with our BX population (Occidental, Duroc and primarily white breed, crossbred pigs). For piglets born below 1 kg birth weight, piglet losses in the BX population were twice that in the Meishan. The Meishan and obese line piglets appear to have greater piglet fat stores at birth in common (Stone et al., 1985), and other literature supports this as a factor in preweaning survival (Mersmann, 1974). Using reciprocal transfer techniques, we are currently exploring whether the effect in Meishans originates from the maternal environment generated by the Meishan uterus, or is a characteristic of the piglets themselves. These results suggest that one of the best ways to select for preweaning survival might be to develop noninvasive ways to measure piglet energy stores at birth. Some possibilities we are considering are bioelectric impedance (related to body fat) and piglet body temperature shortly after birth (related to energy stores). In support of this contention, piglet body temperature 1 h after birth has been reported to be a significant risk factor for preweaning loss in a Meishan F2 population of pigs (Casellas et al., 2004).

In conclusion, the number of piglets weaned is influenced by the number of fully formed piglets, the stillbirth rate and preweaning survival. All of these factors appear to be significantly influenced by the size of the fetus/piglet, which is likely controlled by uterine space/placental size, placental efficiency and fetal nutrient usage. Over and above this, the stillbirth rate is strongly influenced by the farrowing process, and preweaning survival is likely modulated by piglet energy stores at birth. Genetic markers associated with these traits, or simple phenotypes that are well correlated with these traits, are needed to identify superior animals.

19

19

References Anderson, LH, Christenson, LK, Christenson, RK, Ford, SP. 1993. Investigations into the control of litter size in swine: II. Comparisons of morphological and functional embryonic diversity between Chinese and American breeds. J. Anim. Sci. 71:1566-1571. Bennett, GL, Leymaster, KA. 1989. Integration of ovulation rate, potential embryonic viability and uterine capacity into a model of litter size in swine. J. Anim. Sci. 67:1230-1241. Biensen, NJ, Wilson, ME, Ford, SP. 1998. The impact of either a Meishan or Yorkshire uterus on Meishan or Yorkshire fetal and placental development to days 70, 90 and 110 of gestation. J. Anim. Sci. 76: 2169-2176. Casellas, J, Noguera, JL, Varona, L, Sanchez, A, Arque, M, Piedrahita, J. 2004. Viability of Iberian x Meishan F2 newborn pigs. II. Survival analysis up to weaning. J. Anim.Sci. 82:1925-1930. Cho, SJ, Dlamini, BJ, Klindt, J, Schwabe, C, Jacobson, CD, Anderson, LL. 1998. Antiporcine relaxin (antipRLX540) treatment decreases relaxin plasma concentration and disrupts delivery in late pregnant pigs. Anim. Reprod. Sci. 52:303-316. Christenson, RK. 1993 Ovulation rate and embryonic survival in Chinese Meishan and White Crossbred pigs. J. Anim. Sci. 71:3060-3066. First, NL, Bosc, MJ. 1979. Proposed Mechanisms controlling parturition and the induction of parturition in swine. J. Anim. Sci. 48:1407-1421. Gall, MA, Day, BN. 1987. Induction of parturition in swine with prostaglandin F2α, estradiol benzoate and oxytocin. Theriogenology 27:493-505. Geisert, RD, Brookbank, JW, Roberts, RM, Bazer, FW. 1982. Establishment of pregnancy in the pig: II. Cellular remodeling of the porcine blastocyst during elongation on day 12 of pregnancy. Biol. Reprod. 27:941-955. Geisert, RD, Zavy, MT, Moffatt, RJ, Blair, RM, Yellin, T. 1990. Embryonic steroids and the establishment of pregnancy in pigs. J. Reprod. Fertil. Suppl. 40:293-305. Gilbert, CL, Goode, JA, McGrath, TJ. 1994. Pulsatile secretion of ocytocin during parturition in the pig: temporal relationship with fetal expulsion. J. Physiol. 475:129-137. Guthrie, HD. 1985. Control of time of parturition in pigs. J. Reprod. Fert. Suppl. 33:229-244. Haley, CS, Lee GJ. 1993. Genetic basis of prolificacy in Meishan pigs. J. Reprod. Fertil. Suppl. 48:247-259. Ishii, I, Fulushima, N, Ye, X, Chun, J. 2004. Lysophospholipid receptors: signaling and biology. Annu. Rev. Biochem. 73:321-354. Kayser, JP, Kim JG, Cerny, RL, Vallet, JL. 2006. Global characterization of porcine intrauterine proteins during early pregnancy. Reproduction. 131:379-388. Knudson, CB, Knudson, W 1993. Hyaluronan-binding proteins in development, tissue homeostasis, and disease. FASEB J. 7:1233-1241. Laurent, TC, Fraser, JRE. 1992. Hyaluronan. FASEB J 6:2397-2404.

20

20

Leiser, R. Dantzer, V. 1988. Structural and functional aspects of porcine placental microvasculature. Anat. Embryol. 177:409-419. Mersmann, HJ. 1974. Metabolic patterns in the neonatal swine. J. Anim. Sci. 38:1022-1030. Mesa, H, Safranski, TJ, Fischer, KA, Cammack, KM, Lamberson, WR. 2005. Selection for placental efficiency in swine: genetic parameters and trends. J. Anim. Sci. 83:983-991. Milligan, BN, Dewey, CE, De Grau, AF. 2002. Neonatal-piglet weight variation and its relation to pre-weaning mortality and weight gain on commercial farms. Prev. Veter. Med. 56:119-127. Pearson, PL, Klemcke, HG, Christenson, RK, Vallet, JL. 1998. Uterine environment and breed effects on erythropoiesis and liver protein secretion in late embryonic and early fetal swine. Biol. Reprod. 58:911-918. Pere, MC, Etienne, M. 2000. Uterine blood flow in sows. Effects of pregnancy stage and litter size. Reprod. Nutr. Dev. 40:369-382. Perry, DK, Hannun, YA. 1998. The role of ceramide in cell signaling. Biochim. Biophys. Acta 1436:233-243. Polge, C. 1978. Fertilization in the pig and horse. J. Reprod. Fert. 54:461-470. Roviezzo, F, Del Galdo, F, Abbate, G, Bucci, M, D’Agostino, B, Antunes, E, De Dominicis G, Parente, L, Rossi, F, Cirino, G, De Palma R. 2004. Human eosinophil chemotaxis and selective in vivo recruitment by sphingosine 1-phosphate. Proc. Nat. Acad. Sci. 101:11170-11175. Spicer, AP, Tien, JYL. 2004. Hyaluronan and morphogenesis. Birth Defects Research 72:89-108. Stone, RT, Campion, DR, Klindt, J, Martin, RJ. 1985. Blood parameters and body composition in fetuses from reciprocal crosses of genetically lean and obese swine. Proc. Soc. Exp. Biol. Med. 180:191-195. Town, SC, Patterson, JL, Pereira CZ, Gourley, G, Foxcroft, GR. 2005. Embryonic and fetal development in a commercial dam-line genotype. Anim. Reprod. Sci. 85:301-316. Tuchscherer, M, Puppe, B, Thchscherer, A, Tiemann, U. 2000. Early identification of neonates at risk:traits of newborn piglets with respect to survival. Theriogenology 54:371-388. Vallet, JL 2000 Fetal erythropoiesis and other factors which influence uterine capacity in swine. J. Appl. Anim. Res. 17:1-26. Vallet, JL, Christenson, RK. 2004. Effect of progesterone, mifepristone, and estrogen treatment during early pregnancy on conceptus development and uterine capacity in swine. Biol. Reprod. 70:92-98. Vallet, JL, Christenson, RK, Trout, WE, Klemcke, HG. 1998. Conceptus, progesterone and breed effects on uterine protein secretion in swine. J. Anim. Sci. 76:2657-2670. Vallet, JL, Freking, BA, 2006. Changes in fetal organ weights during gestation after selection for ovulation rate and uterine capacity in swine. J. Anim. Sci. 84:2338-2345. Vallet, JL, Freking, BA, Leymaster, KA, Christenson, RK. 2005. Allelic variation in the erythropoietin receptor gene is associated with uterine capacity and litter size in swine. Anim. Genet. 36:97-103. Vallet, JL, Leymaster, KA, Cassady, JP, Christenson, RK. 2001. Are hematocrit and placental efficiency selection tools for uterine capacity in swine. J. Anim. Sci. 79(Suppl. 2):89(Abstr.)

21

21

Vallet, JL, Klemcke, HG, Christenson, RK. 2002. Interrelationships among conceptus size, uterine protein secretion, fetal erythropoiesis and uterine capacity. J. Anim. Sci. 80:729-737. Vallet, JL, Klemcke, HG, Christenson, RK, Pearson, PL. 2003. The effect of breed and intrauterine crowding on fetal erythropoiesis on day 35 of gestation in swine. J. Anim. Sci. 81:2352-2356. Van Dijk, AJ, Van Rens, BTTM, Van der Lende, T, Taverne, MAM. 2005. Factors affecting duration of the expulsive stage of parturition and piglet birth intervals in sows with uncomplicated, spontaneous farrowings. Theriogenology. 64:1573-1590. Wathes, DC, King, GJ, Porter, DG, Wathes, CM. 1989. Relationship between pre-partum relaxin concentrations and farrowing intervals in the pig. J. Reprod. Fert. 87:383-390. Wilson, ME, Biensen, NJ, Youngs, CR, Ford, SP. 1999. Novel insight into the control of litter size in pigs, using placental efficiency as a selection tool. J. Anim. Sci. 77:1654-1658. Youngs, CR, Christenson, LK, Ford, SP. 1994. Investigations into the control of litter size in swine. III. A reciprocal embryo transfer study of early conceptus development. J. Anim. Sci. 72:725-731.

22

22

23

23

Increasing Sperm Production in Mature Boars via Manipulation of their Neonatal Environment

W.L. Flowers

Department of Animal Science, North Carolina State Univ., Raleigh, N.C. Introduction Most management strategies for increasing the number of spermatozoa per ejaculate have focused on adult boars and have not been very successful (Flowers, 1997). One possible explanation for this is that the biological framework for sperm production as an adult is established early in a boar’s life. In swine, as is the case with most mammalian species, sertoli cells can only support the development of a finite number of germ cells during spermatogenesis (Sharpe et al., 2003). Consequently, the number of sertoli cells typically is thought to be the “rate-limiting” factor in terms of sperm production levels in adults (Amann and Schanbacher, 1983). Sertoli cell proliferation in pigs begins during the prenatal period (McCoard et al., 2002) and continues after birth (Swanlund et al., 1995; Franca et al., 2000). There is some debate over when mitotic activity ends postnatally; however, it is generally accepted that a very active and, probably, critical period occurs during the first 3 weeks after birth (McCoard et al., 2003). Consequently, it is possible that a boar’s potential for sperm production as an adult might be established by the time it is weaned from its mother. Recently, a retrospective study was conducted with a limited number of boars (n=20) in order to begin to look for possible reasons why some boars consistently produce more spermatozoa than others (Flowers, unpublished). It is of interest to note that there were several pairs of littermates in this data set and in several instances they were on opposite ends of the spectrum in terms of number of spermatozoa produced per ejaculate as adults. One of the factors that was highly correlated (r = 0.79) with adult sperm production was a boar’s weaning weight, which, in turn, exhibited a strong inverse relationship (r = -0.85) with the size of the litter from which the boar was weaned. These relationships seem logical since boars weaned in small litters would experience less competition and have the opportunity to consume larger quantities of milk than their counterparts in large litters. The additional nutrition that they received during lactation would coincide with the active period of sertoli cell mitosis and key developmental periods of other male reproductive organs. Thus, it seems physiologically plausible that manipulation of the litter size in which boars nurse may be a way to enhance their sperm production and other aspects of adult reproductive function. Experimental Approach In order to examine the influence of the neonatal environment on adult reproductive performance, 40 terminal-line, crossbred boars were crossfostered at one day of age in such a way that littermates were raised in litters of 6 (n=20) or in litters of 9 or more pigs (n = 20). Boars were selected from birth litters that has equal numbers of gilts and boars and crossfostering was done in such as way that potential milk production difference among sows were minimized. For example, if sow A gave birth to 5 boars and 5 gilts and she was randomly selected to nurse a litter of 9 or more piglets, then 4 of her sons were fostered off to four different sows and she received 4 new boars from other sows, thus, creating a situation in which she nursed 5 different genotypes of boars. The study was conducted with a group of boars born in October and another group born in April creating a Fall and Spring replicate (n = 10 boars / treatment / season). The same sires were used to produce the experimental animals in each replicate. Litters were weaned at 18 days of age and boars were managed according to normal industry practices through the nursery and finishing phases of production. The only exception was that boars were given 4 and 10 square feet of floor space per pig during the nursery and finishing phases, respectively. An important component of the experimental design was that boars from the small (6 pigs) and large litters (> 9 pigs) were co-mingled at weaning. This created a situation in which animals from both treatments were in same pens from weaning through finishing. At 5 months of age, boars were moved from pens and

24

24

housed in individual crates. At 5.5 months of age boars were trained for collection with a dummy sow and collected once per week until they were at least two years of age. Body weight and testicular size were measured at birth, weaning, and every three weeks thereafter for the duration of the study. The length of time and the number of training session required to train boars for collection was recorded. Total number of spermatozoa and semen quality estimates including computer-assisted motility analyses, head/tail morphology, acrosome morphology, and acrosin activity were recorded for each ejaculate. Finally, when boars were 10 months of age, equal numbers of spermatozoa from two, non-littermate, boars, one each treatment, were pooled to make heterospermic insemination doses. The pooled semen was used to breed sows (~n=10 per week per combination) and the paternity of the offspring was determined with DNA fingerprinting techniques. Analysis of variance procedures for repeated measures were used to analyze both categorical (Rosner, 1989) and continuous (Snedecor and Cochran, 1989) variables. The statistical model consisted of treatment (small or large litter), season (Fall or Spring), time, and all appropriate interactions. Results and Discussion The effect of neonatal litter size on body weight and testes size are shown in Figures 1 and 2. For these two variables, there was a season by treatment interaction. In essence, boars raised in small litters weighed more and have larger testicles than boars reared in large litters. However, the difference between the two treatments is much greater in the spring-born than the fall-born replicate. One interpretation of this is that under less than ideal conditions, as is the case for boars that mature during the summer environment (litters born in the spring), the advantage of being raised in a small litter increased exponentially. Training for semen collection began when boars were 24 weeks of age (~ 155 days of age). There were no differences in the number of boars successfully being collected by the end of the training period (Figure 3). However, the overall training period was significantly reduced for boars from small (10 days) than large litters (30 days). These data indicate that boars allowed to nurse in litters of 6 pigs or less have larger testicles and greater libido than boars nursing in litters of 9 or more pigs. The two observations probably are related. Boars raised in small litters had increased testicular size at relatively young ages compared with boars raised in large litters. One interpretation of these data is that testicular maturation and thus testosterone production began earlier. This, in turn, should result in attainment of puberty at a younger age as measured by their desire to mount a dummy sow and be collected. It is particularly impressive that all 20 boars that nursed in small litters mounted and were collected during the first 5 days of the training period. In contrast, only 5 of the 20 boars that nursed in large litters were trained for semen collection during the first 5 days of the training period. Numbers of spermatozoa per ejaculate are also greater in boars raised in small versus large litters. It is important to remember that there is a 6 month difference in age between the fall-born and spring-born replicates, so these data have been analyzed and presented separately (Figure 3). In the spring-born replicate, boars raised in small litters produced about 10 billion more spermatozoa per ejaculate about 75% of the time (61 weeks) between 42 and 112 weeks of age. In contrast, for those born in the fall, boars raised in small litters consistently had 20 billion more spermatozoa per ejaculate than their counterpart raised in large litters beginning at 39 weeks of age until the end of the study ended when they were 2 years of age. From a practical perspective, the collective advantage of being raised in a small litter was an additional 200 insemination doses (600 billion spermatozoa) for boars born in the Spring and an extra 567 insemination doses (1700 billion spermatozoa) for boars born in the Fall. No significant differences among treatments in motility, morphology, acrosome morphology, acrosin activity, or capacitation status were observed (Table 1). Finally, boars raised in small litters sired, on average, around 65% of the piglets resulting from heterospermic inseminations. Consequently, they appear to be more fertile than boars raised in large litters (Table 1). It is difficult to translate this relative advantage into differences in farrowing rate and numbers of pigs born alive at the present time. This is due to the fact that use of heterospermic inseminations and paternity testing of the resulting offspring is a relative assessment of fertility. In other

25

25

words, it can be used to rank boars from most to least fertile. However, this technique cannot really establish whether the most fertile boar produces farrowing rates of 95% or 85%. Nevertheless, these data do indicate that regardless of what the actual fertility level, boars raised in small litters would be higher than those reared in large litters. Amann, R.P., and Schanbacher, B.D. 1983. Physiology of male reproduction. J. Anim. Sci. Suppl. 57, 380-403. Flowers, W.L. 1997. Management of boars for efficient semen production. J. Reprod. Fert. Suppl. 52, 67-78. Franca, L.R., Silva Jr., V.A., Chiarini-Garcia, H., Garcia, S.K., and Debeljnk,L. 2000. Cell proliferation and hormonal changes during postnatal development of the testis in the pig. Biol. Reprod. 63, 1629-1636. McCoard, S.A., Wise, T.H., and Ford. J.J. 2002. Expression levels of müllerian-inhibiting substance, GATA-4 and 17α-hydroxylase/17, 20-lyase cytochrome P450 during embryonic gonadal development in two diverse breeds of swine. J. Endocrin. 175,365-374. McCoard, S.A., Wise, T.H., Lunstra, D.D., and Ford, J.J. 2003. Stereological evaluation of sertoli cell ontogeny during fetal and neonatal life in two diverse breeds of swine. J. Endocrin. 178, 395-403. Rosner, B. 1989. Fundamentals of Biostatistics. PWS-Kent Publishing Company. Boston, MA, U.S.A. Sharpe, R.M., McKinnell, C., Kirlin, C., and Fisher, J.S. 2003. Proliferation and functional maturation of sertoli cells and their relevance to disorders of testis function in adulthood. Reproduction 125, 769-784. Snedecor, G.W., and Cochran, W.G. 1989. Statistical Methods, Eighth Edition. Iowa State University Press. Ames, IA, U.S.A. Swanlund, D.J., N’Diaye, M.R., Loseth, K.J., Pryor, J.L. and Crabo, B.G. 1995. Diverse testicualr responses to exogenous growth hormone and follicle-stimulating hormone in prepubertal boars. Biol. Reprod. 53,749-757.

26

26

Figure 1. Effect of neonatal litter size on body wieght. * Boars raised in litters of 6 weighed more compared with boars raised in litters of > 9 (P < 0.05).

210

72

Body

wei

ght (

kg)

Spring-bornFall-born

*

*

*

> 9 / litter

Age (weeks)

6 / litter

75 78 810

*

72 75 78 810

*

*

*

*200

190

180

0

220

80

9

Body

wei

ght (

kg)

Spring-bornFall-born

*

*

*

Age (weeks)

12 15 180

*

9 12 15 180

*

*

60

40

20

0

100

8

1Bo

dy w

eigh

t (kg

)

Spring-bornFall-born

*

Age (weeks)

2 301 2 30

6

4

2

0

10

27

27

Figure 2. Effect of neonatal litter size on testes size. * Boars raised in litters of 6 had larger testes relative to body size compared with boars raised in litters of > 9 (P < 0.05).

72

Spring-bornFall-born

***

> 9 / litterAge (weeks)

6 / litter

8

Test

es A

rea/

Body

Wei

ght (

%)

75 78 810

*

72 75 78 810

***

*

6

0

10

9

Spring-bornFall-born

***

Age (weeks)

12 15 180

*

9 12 15 180

**

3

Test

es A

rea/

Body

Wei

ght (

%)

2

0

4

1

Spring-bornFall-born

*

Age (weeks)

2 301 2 30

3

Test

es A

rea/

Bod

y W

eigh

t (%

)2

0

4

28

28

Figure 3. Effect of neonatal litter size on boars trained for semen collection on a dummy sow. *More boars raised in litters of 6 were trained to collect from a dummy sow compared with boars raised in litters of > 9 (P < 0.05).

27

*

Age (weeks)28 292624 25

70

Boa

rs C

olle

cted

, cum

ulat

ive

(%)

50

0

90

30

> 9 / litter6 / litter

29

29

Figure 4. Effect of neonatal litter size on number of spermatozoa per ejaculate between 39 and 54 weeks of age. * Boars raised in litters of 6 produced ejaculates with more spermatozoa compared with boars raised in litters of > 9 (P < 0.05). Table 1. Semen Quality and Fertility Estimates from Boars raised in Small or Large Litters during Lactation (mean + s.e.).

Winter

Summer

Variable

6 / litter

> 9 / litter

6 / litter

> 9 / litter

Motile spermatozoa (%)

85.3 + 5.7

86.8 + 6.5

88.4 + 4.3

80.8 + 5.7

Normal morphology (%)

91.3 + 3.4

84.6 + 4.5

88. 3 + 5.1

82.1 + 6.1

Normal acrosome morphology (%)

90.4 + 4.7

83.2 + 3.6

90.6 + 6.1

80.3 + 4.2

Acrosin activity (%) 95.3 + 4.5

90.3 + 3.2

92.8 + 4.1

93.4 + 4.6

Normal capacitation (%) 80.2 + 7.8

70.3 + 6.3

85.3 + 6.9

79.7 + 4.2

Seminal plasma proteins (relative units per ejaculate)

12.2 + 2.4

10.1 + 2.0

12.9 + 2.1

10.7 + 1.4

Proportion of piglets sired in heterospermic matings (%)*

67.3 + 5.7

32.7 + 5.4

63.5 + 4.8

36.5 + 4.3

* Boars raised in litters of 6 sired more pigs than boars raised in litters of > 9 (P = 0.02)

39

Spring-bornFall-born

*

Age (weeks)42 45 51039 42 45 510

100

Spe

rm C

ells

/ ej

acul

ate

(x 1

09 )

80

0

120

5448 5448

*

> 9 /litter6 /litter

30

30

31

31

Cryopreservation of Porcine Gametes: A Chilly Future in the Swine Industry

EM Walters

National Swine Resource and Research Center and Veterinary Pathobiology, University of Missouri, Columbia, MO

Corresponding Author: Eric M Walters, Ph.D. National Swine Resource and Research Center Department of Veterinary Pathobiology

University of Missouri-Columbia S-134b ASRC 920 E Campus Drive Columbia, MO 65211

walterse@missouri.edu 573-882-7234

573-884-7521 (Fax)

Abstract

There are many reasons why cryopreservation of gametes are important: 1) maintenance of genetic diversity in domestic and wild species populations (Wildt 1992; Wildt 1997; Critser and Russell 2000), 2) facilitating the distribution of “genetically superior” domestic species lines, 3) treatment of human infertility (Kuczynski et al. 2001; Ranganathan et al. 2002; Tash et al. 2003; Agarwal et al. 2004; Nalesnik et al. 2004), and 4) genetic banking of genetically modified animal models of human health and disease (Critser and Russell 2000; Knight and Abbott 2002). Although cryopreservation of gametes have been routine in many other industries such as the dairy industry. The swine industry is still in it infancy. Birth of live offspring has been reported from cryopreserved sperm and embryos, but success is still extremely low. From an industry perspective the low success rate has too much of an economic impact that the integration of the technology has been slow. However, the improvements in the technologies are slowly improving the pregnancy rates, farrowing rates and litter size. Integration of cryopreservation in to the swine industry is coming and will have a huge impact on movement of genetic material internationally and domestically. Introduction The swine industry has continued to change with the demands of the public and has become a worldwide industry. With this increase distance between grand-parent herds and commercial herds the need to utilize reproductive technologies has increased. Most of the interest has been with cryopreservation of porcine gametes largely as an easy way to move germplasm from one farm to another farm. In addition with the outbreak of Foot and Mouth disease in the UK in 2001, cryopreservation of porcine gametes is seen as a method for disease elimination if proper washing techniques are utilized. However, the utilization of these technologies is currently limited by the on-farm success of these techniques. Embryo cryopreservation in many domestic animals is routine, however in the pig there has been limited success (Dobrinsky 2001c). Pig embryos have an extreme sensitivity to hypothermic exposure which impedes the ability to use conventional slow cooling protocols. However, development of vitrification methods using an open pulled straw (Vajta et al. 1997) has increased the survivalability of porcine embryos but still has limitations for the swine industry. The biggest limitation of the embryo cryopreservation for the swine industry is the reduced farrowing rates and litter size, and lack of nonsurgical embryo collection and transfer procedures Similar to embryo cryopreservation, porcine sperm cryopreservation success is limited again due to the extreme sensitivity of pig sperm to hypothermic exposure. Despite the potential for a huge impact on the industry the use of frozen-thawed semen is >1% of the AI being performed (Wagner and Thibier

32

32

2000) because of the reduced economics compared to either fresh or liquid-cooled semen. Currently, the use of frozen–thawed boar sperm during insemination results in a reduction in farrowing rates and litter size by 50% and three piglets per litter, respectively (Johnson 1985). Cryopreservation of porcine embryos Although porcine embryos have been successful frozen and produced live offspring (Dobrinsky 2001), the impact of cryopreserved embryos on the swine industry is limited. This limitation is due to the difficulty collecting in vivo derived pig embryos, their hypothermic sensitivity (i.e. the cryopreservation procedure), and the lack of a commercially viable non-surgical embryo transfer procedure (Martinez et al. 2004). Pig embryos have an extreme sensitivity to cooling so have limited the success of cryopreservation to vitrification versus slowing cooling. Peri-hatching porcine embryos have the greatest survival rate (Dobrinsky 2001; Dobrinsky 2001a) but are not currently used in the industry due to guidelines set forth by the International Embryo Transfer Society which restricts the cryopreservation to zona intact embryos for international and domestic shipping (Stringfellow 1998). Risk of disease transmission increases as the zona-free embryos becomes exposed to the natural surroundings. However, currently most of the embryo cryopreservation work requires some manipulation of the embryo prior to cryopreservation which also severely limits its impact in the swine industry. Currently most of the success with porcine embryo cryopreservation involves damage to the zona pellucida independent of embryonic stage. Either the zona is completely removed as with the peri-hatching blastocyst or a small incision is made in the zona to delipate (remove the lipid) the embryos. The reason that much of the work requires manipulation of the embryo and damaging of the zona prior to the cryopreservation protocol is the lipid content of the embryo. Pig embryos have a large amount of lipid compared to other species, it was found that removal of the lipid increased the survival of cryopreserved porcine embryos (Nagashima et al. 1995). Typically the intracellular lipid content of porcine embryos is composed of triacylglycerols (Sturmey and Leese 2003). Removal of the lipid from the embryos requires centrifugation and micromanipulation which compromises the zona pellucida thus increasing the risk of disease exposure and transmission. However, currently there is work being done to remove the lipid without compromising the zona, either by polarize of the lipid (centrifugation without micromanipulation) or chemical delipation. Polarization of lipid in the pig embryos is a technique to minimize damage to the zona prior to cryopreservation. Polarization of the lipid involves centrifugation of the embryos at a relatively high speed to cause the lipid to collect at the bottom portion of the embryo. Initially Cameron et al., ((Cameron et al. 2000) reported the birth of the first vitrified zona intact pig embryos, however, the pregnancy rate and embryo survival was extremely low. Beebe et al., (Beebe et al. 2005) modified the freezing protocol by changing the base medium and decreasing the plunging temperature to -204o C from -196o C which resulted in an increase in pregnancy rate and embryo survival. In a large on-farm trial, Beebe et al., ((Beebe et al. 2005) reported that using this improved cryopreservation protocol; the litter size with vitrified embryos was 8.2 total born and 7.7 born alive. Chemical delipation of the pig embryos is a new technique that is being developed to keep embryos zona intact. Lipolysis of triacylglycerols is regulated by many hormones but there are also several chemicals that are capable of lipolysis. Forskolin is a chemical that has lipolytic activity that have been used to chemically delipate pig embryos prior to cryopreservation. Men et al., ((Men et al. 2006) reported the use of Forskolin for chemical delipation of pig embryos prior to cryopreservation with increased survival. They state pig blastocyst treated with Forskolin and an apoptosis inhibitor approximately 50% of the embryos survived vitrificaiton compared to 23% survival (Men et al. 2006). However, they did not report any embryo transfer data or live offspring. Although survival rate increased with the treatment, for an industry impact it must keep the pregnancy and farrowing rates, and litter size to a “normal” average. Litter sizes of 8.2 total born and 7.7 born alive, embryo cryopreservation integration into the swine industry is not too far away. Cryopreservation of Boar Sperm Preservation of boar sperm was developed in the 1970s (Pursel and Johnson 1975), however the method used was different than that for other species. Specifically in 1975, Pursel and Johnson developed a “pellet” method that was successful in freezing boar sperm. First, samples were cooled to 5o C at a rate of 0.22o C/ min. At this temperature, cooled media containing extender and glycerol was

33

33

added. After the addition of glycerol, aliquots of the samples were placed directly on a block of dry ice (-79o C) and then plunged to liquid nitrogen (LN2; -196o C). This pellet method was relatively effective in terms of post-thaw motility but the major drawback was the inability to individually label the pellets and the difficulty involved with shipment of the samples. In recent years, other methods have been developed such as “maxi” (5 ml) and “mini” (0.25 or 0.5 cc) straws, which allow individual identification and ability to ship germplasm domestically and internationally (Bwanga et al. 1990; Bwanga 1991). There has been limited progress in boar sperm cryopreservation in the past several years due to the fact that most of the work uses an empirical approach instead of a fundamental cryobiology approach. Fundamental cryobiology approach investigates and takes into account the biophysical characteristics of the sperm when developing cryopreservation protocols. Successful sperm cryopreservation requires maintaining the post-thaw structural and functional integrity. Maintaining functional integrity is critical, the compartments (i.e. acrosome, flagella, midpiece) of sperm will be affected by cryopreservation differently and need to be fully protected so that frozen-thawed sperm can undergo normal fertilization under in vivo conditions. While motility may be protected at a high level, acrosome integrity may be severely damaged under a similar physical alteration such as osmotic stress (Gilmore et al. 1998; Agca et al. 2002; Guthrie et al. 2002; Walters et al. 2005). The semipermeable nature of the plasma membrane that surrounds sperm cells causes volume changes when exposed to anisosmotic solutions. The degree of volume response to specific anisosmotic solutions is unique for each cell type. Therefore, knowledge of the volume response to anisosmotic conditions relies on a fundamental understanding of biophysical characteristics of the cells of interest. Several projects have begun to gain the understanding of the physical and biophysical characteristics of boar spermatozoa, which is critical to the development and optimization of cyropreservation protocols. With the knowledge of fundamental cryobiological properties associated with osmotic changes such as: 1) permeability to water (Lp) and cryoprotectants (Ps); 2) activation energies (Ea); and 3) osmotic tolerance limits (OTL) (Gilmore et al. 1998), we can begin to mathematically model cryopreservation protocols to determine the optimal addition and removal of CPAs, as well as cooling and warming rates. There is a potential for osmotic injury to the cell with equilibration of high concentrations of permeating cryoprotective agents (CPA) which causes the cell to shrink and swell in response to the influx and efflux of water and CPA. Gilmore et al., (Gilmore et al. 1998) reported that spermatozoa from boars have reduced osmotic tolerance relative to sperm from other mammalian species. In order to maintain 90% motility, the cell volume excursions must be maintained between 99% and 101% of the initial isosmotic volume, which is much narrower than the osmotic tolerance of human sperm (75% and 110% of their isosmotic volume) (Gao et al. 1995; Gilmore et al. 1998). Further studies have reported that boar sperm OTL can be extended with the addition of extender components such as cholesterol (Walters et al., 2006 unpublished data). It is believed that the extender components extend OTL by altering membrane permeability characteristics as well as the potentially the temperature dependences of these characteristics (Walters et al., 2006 unpublished data). During the cryopreservation procedure, loss of motility is hypothesized to be associated with one or more cellular injuries. Cellular injury resulting from concentrated solutions during the cryopreservation procedure is associated with either 1) an osmotic effect, or 2) a solution effect. Solution effects are a collective characterization of cellular injury as a result of concentration of solutes as a result of ice formation (Mazur et al. 2000). It has been suggested that solution effects are exacerbated by slow cooling rates due to the fact that the exposure time to the highly concentrated solution is increased. On the other hand, the osmotic effect, results in cellular injury due to the shrinkage and swelling of the cell in response to changes in the extracellular osmolality. Understanding of the osmotic effects on boar sperm from different genetic backgrounds, coupled with membrane permeability parameters, one can engineer CPA addition and removal procedures specifically tailored to each strain’s sensitivity, and begin to development of breed-specific cryopreservation protocols. As stated before most of the work has used an empirical approach to develop cryopreservation protocols for the boar. Currently, methods are being developed to freeze boar sperm by alterations of the freezing medium composition such as the addition of the antioxidants (Funahashi and Sano 2005), various forms of packaging the semen for cryopreservation (Bwanga 1991), and storage prior to cryopreservation (Guthrie and Welch 2005). There has been an effort to investigate the effects of reactive oxygen species on cryopreservation of boar sperm by the addition of antioxidants to the extender prior to freezing. In addition there is a large boar to boar variation as well as the intra-boar (ejaculate variation) in the ability of the sperm to undergo cryopreservation.

34

34

Recently there has been a desire to develop a simple and effective test for determining “good” versus “bad” freezers for a way for the industry to decide which boars to keep in the herd. Thurston et al., (Thurston et al. 2002) used amplified restricted fragment length polymorphism technology to find 16 different molecular markers linked to freezability that potentially could be used for identifying inter-boar variation. In addition, Thurston et al., (Thurston et al. 2002) reported that the inter-boar variation may be genetically predetermined as they investigated differences between three breeds of pigs (Landrace, Large White, Duroc). As of now, there is no good method to determine if boars are “good” or “bad” freezers during selection. In the swine industry, producers will limit the use frozen-thawed semen if they have to thaw 10-15 0.5cc straws to achieve the desired AI dose. However, if the producer can thaw one flatpack (containing 5ml of sperm) and dilute to achieve an AI dose in combination with good farrowing rates and litter sizes, frozen-thawed boar sperm will have a huge impact on the industry. However, currently the dose of frozen thawed sperm is 5-6 x 109 which is twice the “normal” AI dose, as a large percentage of the sperm are lost during the freeze-thaw procedure. Furthermore, the lost of sperm is not limited to the cryopreservation procedure, frozen thawed sperm have a limited life span in the female tract. With this limited life span of frozen-thawed sperm, the need for more accurate heat detection and proper AI technique increases. There are alternative methods to improve the fertility of frozen thawed sperm such as timed AI, and deep uterine insemination (DUI). One of the advantages that DUI offers is the use of a low dose insemination with the frozen-thawed sperm. But a disadvantage of DUI is timing of insemination relative to the ovarian status of the female. This timing between ovulation and insemination may account for some of the differences between farms using frozen-thawed semen. Bolarin et al., (Bolarin et al. 2006) found that using DUI with frozen thawed sperm that peri-ovulatory (some ovulation had occurred) ovarian status of the females increased pregnancy, farrowing rates and litter size compared to either pre-ovulatory or presence of corpus hemorrhagica. In this study, Bolarin et al., (Bolarin et al. 2006) compared two farms with different management styles and found there was a difference between the two farms in terms of success with DUI in combination frozen thawed sperm. One of the big differences between the two farms was the ovarian status of the females used for this trial as a larger percentage of the females were peri-ovulatory at one farm versus the other farm. The different management styles between the farms probably accounts for the differences seen in the results with DUI in combination with frozen-thawed sperm. In farm A (farm with the largest peri-ovulatory group) boar exposure was minimal as there was no “habituation” of the boars with the females, however, in farm B there was continuous boar exposure (Bolarin et al. 2006). Suggesting that management practices in particular boar exposure and heat detection is critical for DUI in combination with frozen thawed sperm. Conclusions The cryopreservation of porcine gametes has made huge improvements in the last several years however; the potential impact in the swine industry has been limited. There are still many factors that have to be addressed before cryopreservation of porcine gametes will be beneficial to the swine industry but steps are being taken to make this a reality. In addition, there several reproductive technologies such as nonsurgical embryo collection and transfer procedures that has to be optimized as they will be critical for the future of cryopreservation of porcine gametes. The integration of cryopreservation in the swine industry has a bright and chilly future. References

Agarwal, A., P. Ranganathan, et al. (2004). "Fertility after cancer: a prospective review of assisted reproductive outcome with banked semen specimens." Fertil Steril 81(2): 342-8.

Agca, Y., J. Gilmore, et al. (2002). "Osmotic Characteristics of Mouse Spermatozoa in the Presence of Extenders and Sugars." Biol Reprod 67(5): 1493-1501.

Beebe, L. F., R. D. Cameron, et al. (2005). "Changes to porcine blastocyst vitrification methods and improved litter size after transfer." Theriogenology 64(4): 879-90.

35

35

Bolarin, A., J. Roca, et al. (2006). "Dissimilarities in sows' ovarian status at the insemination time could explain differences in fertility between farms when frozen-thawed semen is used." Theriogenology 65(3): 669-80.

Bwanga, C. O. (1991). "Cryopreservation of boar semen. I: A literature review." Acta Vet Scand 32(4): 431-53.

Bwanga, C. O., M. M. de Braganca, et al. (1990). "Cryopreservation of boar semen in mini- and maxi-straws." Zentralbl Veterinarmed A 37(9): 651-658.

Cameron, R. D., L. F. Beebe, et al. (2000). "Piglets born from vitrified early blastocysts using a simple technique." Aust Vet J 78(3): 195-6.

Critser, J. K. and R. J. Russell (2000). "Genome resource banking of laboratory animal models." ILAR 41: 183-186.

Dobrinsky, J. R. (2001). "Cryopreservation of swine embryos: a chilly past with a vitrifying future." Theriogenology 56(8): 1333-44.

Dobrinsky, J. R. (2001a). "Cryopreservation of pig embryos: adaptation of vitrification technology for embryo transfer." Reprod Suppl 58: 325-33.

Dobrinsky, J. R. (2001c). "Cryopreservation of pig embryos: adaptation of vitrification technology for embryo transfer." Reprod Suppl 58: 325-33.

Funahashi, H. and T. Sano (2005). "Select antioxidants improve the function of extended boar semen stored at 10 degrees C." Theriogenology 63(6): 1605-16.

Gao, D. Y., J. Liu, et al. (1995). "Prevention of osmotic injury to human spermatozoa during addition and removal of glycerol." Hum. Reprod. 10(5): 1109-1122.

Gilmore, J., J. Liu, et al. (1998). "Determination of plasma membrane characteristics of boar spermatozoa and their relevance to cryopreservation." Biol Reprod 58(1): 28-36.

Gilmore, J. A., J. Liu, et al. (1998). "Determination of plasma membrane characteristics of boar spermatozoa and their relevance to cryopreservation." Biol Reprod 58(1): 28-36.

Guthrie, H. D., J. Liu, et al. (2002). "Osmotic Tolerance Limits and Effects of Cryoprotectants on Motility of Bovine Spermatozoa." Biol Reprod 67(6): 1811-1816.

Guthrie, H. D. and G. R. Welch (2005). "Impact of storage prior to cryopreservation on plasma membrane function and fertility of boar sperm." Theriogenology 63(2): 396-410.

Johnson, L. (1985). Fertility results using frozen boar spermatozoa: 1970–1985. Deep freezing of boar semen. L. Johnson and K. Larsson. Uppsala, Swedish Univ Agric Sci: 199-222.

Knight, J. and A. Abbott (2002). "Full House." Nature 417: 785-786.

Kuczynski, W., M. Dhont, et al. (2001). "The outcome of intracytoplasmic injection of fresh and cryopreserved ejaculated spermatozoa--a prospective randomized study." Hum Reprod 16(10): 2109-13.

Martinez, E. A., J. N. Caamano, et al. (2004). "Successful nonsurgical deep uterine embryo transfer in pigs." Theriogenology 61(1): 137-46.

36

36

Mazur, P., I. I. Katkov, et al. (2000). "The Enhancement of the Ability of Mouse Sperm to Survive Freezing and Thawing by the Use of High Concentrations of Glycerol and the Presence of an Escherichia coli Membrane Preparation (Oxyrase) to Lower the Oxygen Concentration*1, *2." Cryobiology 40(3 SU -): 187-209.

Men, H., Y. Agca, et al. (2006). "Improved survival of vitrified porcine embryos after partial delipation through chemically stimulated lipolysis and inhibition of apoptosis." Theriogenology 66(8): 2008-16.

Nagashima, H., N. Kashiwazaki, et al. (1995). "Cryopreservation of porcine embryos." Nature 374(6521): 416.

Nalesnik, J. G., E. S. Sabanegh, Jr., et al. (2004). "Fertility in men after treatment for stage 1 and 2A seminoma." Am J Clin Oncol 27(6): 584-8.

Pursel, V. G. and L. A. Johnson (1975). "Freezing of boar spermatozoa: fertilizing capacity with concentrated semen and a new thawing procedure." J Anim Sci 40(1): 99-102.

Ranganathan, P., A. M. Mahran, et al. (2002). "Sperm cryopreservation for men with nonmalignant, systemic diseases: a descriptive study." J Androl 23(1): 71-5.

Stringfellow, D. A. (1998). Recommendations for the sanitary handling of in vivo derived embryos. Manual of the International Embryo Transfer Society. D. A. Stringfellow and S. M. Seidel. Savoy, International Embryo Transfer Society: 1-170.

Sturmey, R. G. and H. J. Leese (2003). "Energy metabolism in pig oocytes and early embryos." Reproduction 126(2): 197-204.

Tash, J. A., L. D. Applegarth, et al. (2003). "Postmortem sperm retrieval: the effect of instituting guidelines." J Urol 170(5): 1922-5.

Thurston, L. M., K. Siggins, et al. (2002). "Identification of Amplified Restriction Fragment Length Polymorphism Markers Linked to Genes Controlling Boar Sperm Viability Following Cryopreservation." Biol Reprod 66(3): 545-554.

Vajta, G., P. Holm, et al. (1997). "Vitrification of porcine embryos using the Open Pulled Straw (OPS) method." Acta Vet Scand 38(4): 349-52.

Wagner, H. G. and M. Thibier (2000). World statisitics for artifical insemination in small ruminants ans swine. 14th International Congress on Animal Reproduction, Stockholm, Sweden.

Walters, E., H. Men, et al. (2005). "Osmotic tolerance of mouse spermatozoa from various genetic backgrounds: Acrosome integrity, membrane integrity, and maintenance of motility." Cryobiology 50: 193-205.

Wildt, D. E. (1992). "Genetic Resource Banks for Conserving Wildlife Species: Justification, Examples and becoming Organized on a Global Basis." Anim Reprod Sci 28: 247-257.

Wildt, D. E. (1997). Genome Resource Banking: Impact on Biotic Conservation and Society. Reproductive Tissue Banking. A. M. Karow and J. K. Critser. San Diego, Academic Press: 399-440.

37

37

SELECTION LINES FOR RESIDUAL FEED INTAKE IN YORKSHIRE SWINE

Weiguo Cai1, David Casey2, and Jack Dekkers1

1 Department of Animal Science, Iowa State University 2 Pig Improvement Company, Genus plc.

Introduction

Feed for the growing phase is the largest variable cost in swine production. Large differences in FI exist and a substantial proportion (~30%) of these are related to genetics. Estimates of heritability of FI average 0.3, ranging from 0.12 to 0.74 (Clutter and Brascamp 1998). Although FE (kg product/kg feed) has improved to some degree by selection for growth and reduced backfat, further improvements require direct selection on FI. This is, however, prohibited by the difficulty and expense of recording FI on large numbers of animals, but possible if the genes responsible for differences in FI and FE are known. A thorough understanding of mechanisms that control FI and energy metabolism will be needed to utilize genetic information on FI in a manner that will enhance production efficiency. Although FI is genetically related to the economically important traits of growth and backfat, these relationships are not perfect; estimates of genetic correlations average 0.65 (0.32-0.89) with growth rate and 0.37 (0.08-0.59) with backfat thickness (Clutter and Brascamp 1998). Thus, although a large proportion (36-64%) (Luiting 1998) of variation in FI is related to production traits, there is considerable variation that is independent of growth and composition. This is referred to as residual FI (RFI), i.e. feed consumed over and above expected requirements for production and maintenance (Luiting, 1990). Variation in RFI is not utilized in genetic selection for growth and composition but is heritable; estimates in the pig range from 0.15 to 0.40 (Foster et al. 1983, Mrode and Kennedy 1993, Von Felde et al. 1996, Johnson et al. 1999). Factors that contribute to genetic variation in RFI include feeding behavior, nutrient digestion, maintenance requirements, and energy homeostasis and partitioning (Luiting 1998). Genetic differences in the ability to digest nutrients are small but differences in maintenance requirements play a major role (Luiting 1998). Although reduced maintenance requirements are desirable for improved FE, this may result in reduced fitness and increases susceptibility to stressors and diseases (Rauw et al. 1998). To enable the study of the genetic and physiological basis of feed efficiency, a selection experiment for RFI was initiated. In this paper, we evaluate response to selection in the first 3 generations.

38

38

Mar 01 27 sires x 72 dams Yorkshire

Generation 0 Aug 01 Parity 1 born

Random selection 1 male/litter 14 males

females 1.5 females/litter LOW RFI LINE CONTROL LINE

Nov 01 88 males on FIRE 14 males 189 females 51 females

Feb 02 Parity 2 born

Apr 02 Sibs of Evaluation and selection Random selection selected boars 12/64 boars EBVRFI=-.083 kg/d 12 boars EBVRFI=0 kg/d 67/154 gilts EBVRFI=-.039 kg/d 40 gilts EBVRFI=0 kg/d

May 02 90 gilts on FIRE

Generation 1 Sept 02 Parity 1 born Dec 02 90 males on FIRE 25 males 159 females EBVRFI=-.056 kg/d 49 females Mar 03 Parity 2 born

May 03 Sibs of Evaluation and selection Random selection selected boars 12/64 boars EBVRFI=-.113 kg/d 69/155 gilts EBVRFI=-.077 kg/d

June 03 90 gilts on FIRE

Generation 2 Nov 03 Parity 1 born Jan 04 90 males on FIRE 30 males 160 females EBVRFI=-.095 kg/d 45 females

Apr 04 Parity 2 born

June 04 Sibs of Evaluation and selection Random selection selected boars

July 04 90 gilts on FIRE

Figure 1. Outline of the selection experiment for RFI

Materials and Methods Using purebred Yorkshire pigs, a selection experiment for RFI was started in 2001. An outline of the experiment and its progress is in Fig.1. Starting with random allocation of littermates, in each generation, electronically measured feed intake, body weight, and ultrasound backfat are evaluated from ~40 to ~115 kg on 90 boars from first parity LRFI sows and 90 gilts from second parity LRFI sows. Low RFI line boars are raised on FIRE© feeders for electronic recording of FI in pens. Database and edit systems to handle the large amounts of data (~6 meals/d/pig; Casey and Dekkers 2001) were developed (Casey 2003). RFI is estimated by adjusting FI for growth and backfat (Mrode and Kennedy, 1993) and selection is on mixed linear model estimates of breeding values for RFI. Full- or half-sisters of the selected boars, produced at the 2nd parity of their dams, are evaluated for RFI to provide additional data. Following evaluation of first parity boars, ~12 LRFI boars and 70 gilts are selected to produce ~50 litters for the next generation. About 30 control line litters are produced by random mating. Selection is on EBV for RFI from animal model analysis of average daily feed intake (FI), with group and sex (fixed), pen within group (random), and covariates for on- and off-test weight and age, for average daily gain (ADG) and backfat (BF). The same model was used to estimate heritability of RFI, FI, and ADG and BF based on data on 638 LRFI pigs with FI data across generations.

Since FI is routinely not recorded in the control line, line differences for RFI, FI, ADG and BF were evaluated after 3 generations using phenotypic data on 49 LRFI and 38 control gilts that were simultaneously evaluated for FI from ~40 to ~70 kg. Data were analysed with a mixed model with line as a fixed effect.

Results and Discussion Estimates of heritabilities and line differences are in Table 1. Estimates of phenotypic and genetic correlations are in Table 2. Residual feed intake contributed nearly 50% of phenotypic variation in feed intake, the rest being explained by variation in growth rate and backfat. Heritability estimate of FI, ADG and BF were within the range observed in literature. RFI had a substantial heritability (0.30). Selection line gilts had significantly lower RFI (93 g/d) and FI (122 g/d) than the control line with no significant change in ADG and BF, although there was a tendency for the LRFI line to have lower growth. Estimates of response based on direct comparison of lines were Estimates of response based on direct comparison of lines were lower than estimates based on average EBV from analysis of data observed on selection line animals only. This may because RFI was evaluated over a different time period and on gilts versus boars.

39

39

Table 1. Estimates of phenotyic standard deviations, heritability and differences between the LRFI and control lines based on estimates of breeding values and direct phenotypic comparison.

Line difference (LRFI-control) estimated based on

Trait

Phenotypic standard deviation

Heritability + st.error

EBV in LRFI line Phenotypic comparison RFI (g/d) 130 0.30 + 0.09 -123 -93*** FI (g/d) 187 0.46 + 0.10 -153 -123***

ADG (g/d) 79 0.33 + 0.09 -13 -22NS

BF (mm) 3 0.67 + 0.11 -0.47 0.063 NS *** significant at P<.01 NS Not significant at P<.10

Table 2. Estimates of heritability (on diagonal), phenotypic correlations (above diagonal), and genetic correlations (below diagonal) based on bivariate analysis of data from four generations.

Trait RFI FI ADG BF LMA IMF RFI 0.33 0.51 -0.01 -0.01 -0.1 0.04 FI 0.71 0.40 0.65 0.44 -0.03 0.10

ADG 0.15 0.74 0.33 0.29 0.11 0.18 BF -0.01 0.55 0.40 0.61 -0.05 0.04

LMA -0.265 -0.10 0.19 -0.05 0.62 0.07 IMF 0.15 0.23 0.27 -0.05 0.26 0.53

Summary and Implications A line of Yorkshire pigs was selected for 3 generations for reduced residual feed intake (RFI), a measure of feed efficiency defined as feed consumed over and above average requirements for maintenance and growth. Heritability estimates of RFI, feed intake, growth, and backfat were 0.30, 0.46, 0.33, and 0.67. Comparison of performance of gilts from the selected line (n=49) to those of a randomly selected control line (n=38) from ~40 to ~70 kg showed that selection had significantly decreased feed intake by 123 g/d. There were no significant differences in average daily gain and backfat between the lines, although the selection line tended to have 22 g/d less growth. In conclusion, RFI is a heritable trait and selection for RFI has significantly decreased the amount of feed required for a given rate of growth and backfat. References Casey, D.S., and Dekkers, J.C.M., 2001. Dealing with errors in data from electronic swine feeders.

http://www.extension.iastate.edu/ipic/reports/01swinereports/Breedphys01.html. Casey, D.S., 2003. The use of electronic feeders in genetic improvement programs for swine. Doctoral

Thesis, Iowa State University. Casey, D.S., H.S. Stern, and J.C.M. Dekkers. 2005. Identifying errors and factors associated with errors

in data from electronic swine feeders. J. Anim. Sci. 83: 969-982 Clutter, A.C., and Brascamp, E.W., 1998. Genetics of performance traits. In The Genetics of the Pig (ed.

M.F. Rothschild and A. Ruvinsky), pp. 427-455. CAB Inter., Wallingford, UK. Foster, W.H., Kilpatrick, D.J. and Heaney, I.H., 1983. Genetic variation in the efficiency of energy

utilization by the fattening pig. Animal Production 37: 387-393. Johnson, Z.B., Chewning, J.J., and Nugent, R.A., 3rd, 1999. Genetic parameters for production traits and

measures of residual feed intake in large white swine. J. Anim. Sci. 77: 1679-1685. Luiting, P. 1990. Genetic variation of energy partitioning in laying hens: causes of variation in residual

feed consumption. World's Poultry Science 46: 133-152. Luiting, P., 1998. The role of genetic variation in feed intake and its physiological aspects: results from

selection experiments. In Regulation of Feed Intake (ed. D. van der Heide, E.A. Huisman, E. Kanis, J.W.M. Osse, and M.W.A. Verstegen), pp. 75-87. CABI Publishing, Wallingford, UK.

40

40

Mrode, R.A., and Kennedy, B.W., 1993. Genetic variation in measures of food efficiency in pigs and their genetic relationships with growth rate and backfat. Animal Production 56: 225-232.

Rauw, W.M., Kanis, E., Noordhuizen-Stassen, E.N., and Grommers, F.J., 1998. Undesirable side effects of selection for high production efficiency in farm animals: a review. Livestock Production Science 56: 15-33.

Von Felde, A., Roehe, R., Looft, H., and Kalm, E., 1996. Genetic association between feed intake and feed intake behaviour at different stages of growth of group-housed boars. Livestock Production Science 47: 11-12.

Acknowledgements FIRE feeders used in this experiment were donated by PIC. Funds for development and maintenance of the selection lines were from the Center for Integrated Animal Genomics. Monsanto Co. is acknowledged for providing partial support for Weiguo Cai.

41

41

Comparison of 1980 and Modern Swine Genetic Types when reared on 1980 and Modern Swine Feeding Programs1, 2

J.S. Fix, E. van Heugten, J. P. Cassady, D. J. Hanson, and M.T. See

North Carolina State University Introduction The U.S. pork industry maintains profitability largely through continued improvement in productivity and cost control: however, producing high quality pork products is also important in maintaining consumer confidence. Phenotypic improvements have been reported in USDA data (National Pork Board, 2005) since 1955 for increasing lean mass and reproductive rates of pigs. This study was designed to document for most traits of economic interest improvements the relative contribution that genetic selection and enhancements in feeding programs have made to achieving improvements over the last 25 years. Studies have been conducted in the poultry industry that evaluated the contributions of genetics and nutrition to improvements observed in growth performance and carcass composition (Havenstein et al, 1994a; Havenstein et al, 1994b; Havenstein et al, 2003a; Havenstein et al, 2003b). In addition, these researchers have reported on changes in immune response for these broilers (Qureshi et al, 1994; Cheema et al, 2003). They concluded that genetics, nutrition, and other management changes over the last 44 years have resulted in a broiler that requires approximately one-third the time and over a threefold decrease in the amount of feed consumed to produce a 1,815-g broiler. These data indicate that genetic selection by commercial breeding companies has brought about 85 to 90% of the change that has occurred in broiler growth rate and carcass composition over the past 45 yr. Nutrition has provided 10 to 15% of the changes observed. Recently, Schwab et al. (2006) reported on the effect of long term selection for increased leanness on pork quality in a study comparing purebred Duroc pigs sired by boars representing the 1980s with pigs sired by boars representing the current time period. They found that pigs sired by old time period boars had superior pork quality and concluded that long-term selection for leanness has been at the expense of pork quality. They reported that loin from pigs sired by old time period boars had greater intramuscular fat and visual marbling scores, required less Instron force to compress and had darker visual color scores when compared to loins from pigs sired by contemporary Duroc boars. Materials and Methods A control population for a genetic selection study of commercially available white line animals was formed in 1979 and has been maintained at NCSU since 1989. Fifteen first parity females from this unselected population were mated using frozen semen from Hampshire and Duroc boars that were commercially available in 1979 and 1980 producing twelve litters (7 Duroc sired and 5 Hampshire sired). Three Duroc and Three Hampshire sires were represented. Pigs representative of 2005 commercial genetic lines of similar age to the 1980 genetic line pigs were obtained from a NC swine production company. Pigs were from three farrowing groups. Farrowing groups one, two, and three were included in evaluations of behavior and structure. Farrowing groups one and two were included in evaluations of growth performance, carcass composition, and muscle quality. Farrowing group three was used in a metabolism study evaluating nutrient utilization and odor. Figure 1 describes how the groups were used and the timeline associated with live animal portions of this study.

1 Prepared for the 2006 National Swine Improvement Federation Annual Meeting and Genetics Symposium, December 7th and 8th, Nashville, TN. 2 This work was funded in part by the North Carolina Pork Council, North Carolina Agricultural Foundation, Murphy-Brown LLC, North Carolina Cooperative Extension Service, and North Carolina Agricultural Research Service.

42

42

Assessment of the nutritional contribution to changes in pig structure, growth, composition and quality was accomplished by placing one-half of the pigs from each genetic line on one of two feeding programs typical of those used in 1980 versus industry feeding practices common in 2005. Major differences in nutritional programs included diet formulation, meal diets versus pellets, no-antibiotics versus antibiotics, simple versus phased feeding program, and no synthetic amino acids versus Synthetic amino acids. The 1980 feeding program consisted of four meal diets (lysine from 1.05 to 0.62% and ME from 3262 to 3317 Kcal/kg) based on formulations from the 1978 PIH (Table 1). The 2005 feeding was a seven phase feeding program (lysine from 1.51 to 0.73% and ME from 3428 to 3651 Kcal/kg), pelleted diets, and current diet formulation as used by NC producers (Table 2). Table 1. 1980 Feeding Program Prestarter Starter Grower Finisher

Crude Protein, % 18.3 17.9 15.0 13.3

Metabolizable Energy, kcal/kg 3262 3299 3315 3317

Calcium, % 0.87 0.78 0.67 0.67

Phosphorus, % 0.74 0.70 0.60 0.56

Lysine, % 1.05 0.95 0.75 0.62

Amount budgeted per pig, kg 11.3 15.9 90.7 to market

43

43

Table 2. 2005 Feeding Program Prestarter Starter

1

Starter 2

Grower 1

Grower 2

Finisher 1

Finisher 2

Crude Protein, % 22.6 22.3 22.1 17.9 16.9 14.7 12.0

Metabolizable Energy, kcal/kg

3428 3405 3438 3630 3643 3655 3651

Calcium, % 0.84 0.79 0.72 0.52 0.48 0.43 0.39

Phosphorus, % 0.72 0.68 0.64 0.55 0.47 0.41 0.37

Lysine, % 1.51 1.43 1.36 1.22 1.13 0.94 0.73

CTC, g 400 400 400 400 - - -

Denaguard, g 35 35 35 - - - -

Tylan, g - - - - 20 - -

Stafac, g - - - - - 10 5

Amount budgeted per pig, kg

4.54 9.07 13.61 18.1 45.4 56.7 to market

A factorial design was used to compare pigs representative of 1980 commercial genetics and 2005 commercial genetics when fed feeding programs representative of 1980 and 2005 (Table 3). Pigs (n = 162) were reared at the North Carolina Swine Evaluation Station (Clayton, NC) and assigned to pens (3 pigs per pen) in a 2 x 2 x 2 factorial design for the growth and carcass portion of the study at approximately 7 kg BW. Pigs were housed on solid concrete floors with 5.6 m2 per pen and provided ad libitum access to feed and water. All animal procedures were approved by the Institutional Animal Care and Use Committee of North Carolina State University.

Table 3. Experimental design and number of pens (pigs) per treatment 1980

Genetic Line

2005

Genetic Line

Feeding Program Barrows Gilts Barrows Gilts

1980 6 (18) 7 (21) 7 (21) 7 (21)

2005 5 (15) 8 (24) 7 (21) 7 (21)

Pigs were weighed at the start, end, and every two weeks throughout the study. Feeding programs were provided according to a budget (Tables 1 and 2) and feed allotments were weighed daily to determine ADG, ADFI, and G:F. Average daily gain, ADFI and G:F were calculated for on-test to nursery (OTN) ( 26.9±0.7 kg), nursery to slaughter (NS) and on-test to slaughter (OTS). Fat depth and loin muscle area were measured by real-time ultrasound beginning at week four (Group 1) or five (Group 2) and measured every four weeks thereafter resulting in three measurements. Loin muscle area and backfat were adjusted to 45 kg (1), 70 kg (2) and 95 kg (3). Lean ADG (LADG), lean G:F (LG:F) and ADFI were calculated for on-test to slaughter (OTS) and first realtime ultrasound to slaughter (FSS).

Aggression was evaluated in all pigs (n = 187). Each pig was tested twice with tests being one week apart. Resident intruder tests were conducted by dividing pens in half and moving a resident pig into the empty half. An intruder pig was then introduced. Attack latency was recorded as time from when an intruder pig entered the pen until an attack occurred. Attack latencies were summed for the two tests. If no attack occurred then latency was 180 seconds. A resident-intruder score was given for the number of attacks (0, 1, or 2) during the two tests. Structural correctness and mobility was scored on all pigs (n = 187) on one day (Figure 1). The three evaluators utilized a scoring system based on the Pocket Guide for the Evaluation of Structural, Feet, Leg and Reproductive Soundness in Replacement Gilts (Stalder et al., 2005). Front and rear legs

44

44

were scored on a 1 to 5 scale; 1 = excessive set to the joints, 3 = ideal and 5 = extreme straightness in the joints. Front and rear view structure was scored from 1 to 3; 1= toes out, 2 = ideal, and 3 = toes in. Mobility scores were from 1 to 5; 1 = severely impaired due to injury, 3 = ideal, and 5 = severely impaired due to structure. Pigs were slaughtered by pen on a weekly basis when average pen weight exceeded 116 kg during August - October 2005. Slaughter data was collected at a commercial abattoir (Bailey Slaughterhouse, Bailey NC). At slaughter hot carcass weight and 45 minute pH were measured and viscera and liver samples collected. At 24 h post-mortem carcass length, fat depth at the 1st rib, 10th rib, last rib and last lumbar was measured, as well as, loin muscle area, marbling score, firmness score, Minolta color and ultimate pH. Primal cuts were separated and weighed and color, firmness, and wetness scored on the ham face, as well as, Minolta color. In addition, belly thickness and length was measured. A comparison test of belly firmness (stick test) was conducted by measuring the distance between the outside edges of the belly draped across a pipe. A loin, belly and ham from each pig was collected and frozen for chemical and sensory analysis as well as for evaluation of further processing. Fat samples were collected from the belly, loin and ham of each pig for analysis of fatty acid composition. Percentage drip loss was estimated by hanging duplicate, 100g loin sections removed from between the 9th and 10th rib in a bag for 48 h postmortem. The loin sections were then reweighed and purge loss was determined. Warner Bratzler shear force was measured on cooked loin samples. Chemical analysis (University of Illinois, Champaign IL) of loin samples was conducted to determine percentage fat and moisture. Sensory evaluations of loin samples were conducted by both consumer (100 consumers) and trained panels (6 members). Panelists were screened, selected and trained according to the standards of the American Society of Testing Materials (ASTM) STP 750 (1981) and the methods of Caul (1957) and Cairncross and Sjostrom (1950). Trained flavor and texture descriptive panels were conducted on a 16 point intensity scale. One-half of two pigs randomly selected from each pen was ground and sub-sampled for whole body composition and samples frozen. This included separate samples from the carcass, viscera, and head. In addition, weights of individual organs were measured and stomachs were scored for incidence of gastric ulcers on a 1 to 7 scale. A liver sample was collected from each pig, DNA extracted and genotyped (GeneSeek Inc, Lincoln NE) for HAL 1843 and Rendement Napole (RN) mutation by DNA tests. All pigs in the study were normal (NN) for HAL 1843. For RN 38 pigs form the 1980 genetic line and 8 pigs from the 2005 genetic line were identified as carriers (rn+RN+). Analysis of whole body composition from these samples is underway. An additional twenty-seven pigs (Group 3) were reared in the same 2 x 2 x 2 design in pens of either 3 or 4 and subsequently placed individually in metabolism crates at the Grinnell’s Research Facility (Raleigh, NC) to evaluate nutrient digestion, nutrient excretion, and odor over two-week periods. Full urinary and fecal excretion was collected, sub-sampled and frozen for subsequent analysis. Chemical analysis of excreta and odor panel evaluations are underway. Results and Discussion

Aggression

Genetic line and sex were both found to be significant for attack latency (P < 0.03 and P < 0.04, respectively) and resident intruder score (P < 0.05 and P < 0.01). On average, pigs from the 2005 genetic line attacked 22 seconds faster and 0.20 more times than 1980 genetic line pigs. Gilts attacked 22 seconds faster and 0.27 more times than barrows. Commercial pigs from 2005 were more aggressive toward a foreign pig than commercial pigs from 1980. One interpretation of these results is that selection for increased lean growth rate has resulted in correlated changes in behavior.

Structural Correctness

Pigs from the 2005 genetic line were more relaxed (P < 0.01) in their front leg joints than pigs from the 1980 genetic line. A Genetic line x sex interaction (P < 0.01) was observed where gilts from the 1980 genetic line were the straightest in their front leg joints. 1980 genetic line pigs were straighter (P < 0.01) in their rear leg joints than 2005 genetic line pigs. A feeding program by sex interaction (P < 0.01) indicated barrows fed the 1980 feeding program were more in at their hocks than pigs fed the 2005

45

45

feeding program. Based on a genetic line x feeding program interaction (P < 0.01) pigs from the 2005 genetic line fed the 1980 feeding program were the most mobile. Pigs from the 2005 genetic line were more (P < 0.01) mobile than 1980 genetic line pigs and pigs fed the 1980 feeding program were more (P < 0.05) mobile than pigs fed the 2005 feeding program. Results are summarized in Table 4.

Genetic improvement has occurred in structural correctness and mobility; however, changes in feeding program have resulted in reduced mobility. The improvement via genetics could have come from either direct selection placed on poorer structured individuals or through indirect selection which results from poorer structured individuals having less longevity in the herd and therefore less genetic influence. The reduction in mobility is possibly due to the fact the pigs are heavier muscled and faster growing. Table 4. Effect of Genetic Line and Feeding Program on Structural Correctness

Genetic line 1980 2005 G*FP Genetic

line Feeding program

Feeding program 1980 2005 1980 2005 P-value P-value P-value

Front front 1.85a 1.86a 1.90a 1.82a 0.2336 0.8820 0.3415

Front side 3.27ab 3.45a 3.13b 3.16b 0.3325 0.0081 0.1913

Rear rear 1.36a 1.44a 1.42a 1.42a 0.2588 0.6386 0.3059

Rear side 3.53a 3.40ab 3.27b 3.35b 0.1053 0.0109 0.8026

Mobility 3.73a 3.68ab 3.35c 3.56b 0.0030 <0.0001 0.0469 a-cLeast squares means with no common superscript differ (P < 0.05)

Growth and Performance

The following growth data are summarized in Table 5. Pigs did not differ between treatments for on-test weight (7±0.4 kg). Pigs from the 2005 genetic line had lower (P < 0.01) ADFI and a higher (P < 0.01) G:F for OTN than pigs from the 1980 genetic line. Pigs fed the 2005 feeding program had higher (P < 0.01) ADG and G:F for OTN than pigs fed the 1980 feeding program. For NS pigs from the 2005 genetic line had higher (P < 0.01) ADF and G:F than pigs from the 1980 genetics line. Pigs fed the 2005 feeding program for NS and OTS had higher (P < 0.01) ADG, lower (P < 0.01) ADFI and then higher (P < 0.01) G:F than pigs fed 1980 feeding program. Pigs from the 2005 genetic line had higher (P < 0.01) ADG and G:F than pigs from the 1980 genetic line during OTS. Genetic line x feeding program interactions were observed for NS and OTS ADG where 1980 genetic line pigs versus 2005 genetic line pigs showed 7.01% and 11.83% increases in NS ADG and 6.67% and 12.34% increases in OTS ADG when fed 1980 feeding program versus 2005 feeding program. Slaughter weight (119±1.01 kg) did not differ for genetic lines or feeding program. However pigs from the 2005 genetic line and pigs fed the 2005 feeding program were younger (P < 0.01) at slaughter than pigs from the 1980 genetic and pigs from the 1980 feeding program Lean Gain Genetic line x feeding program interaction (P < 0.01) was observed where pigs from the 1980 genetic line and pigs from the 2005 genetic line had an increase in OTS ADG of 7.00% and 17.06% when fed 1980 feeding program versus 2005 feeding program. 2005 genetic line pigs had higher (P < 0.01) LADG for OTS and FSS, did not differ in ADFI for either and subsequently had higher (P <0.01) LG:F for OTS and FSS than pigs from 2005 genetic line. Pigs fed the 2005 feeding program had higher (P < 0.01) LADG OTS, lower (P < 0.01) ADFI OTS and FSS and as a result has higher (P < 0.01) LG:F OTS and FSS. Data are summarized in table 5.

46

46

Table 5. Effect of Genetic Line and Feeding Program on Growth and Performance a-dLeast squares means with no common superscript differ (P < 0.05)

Realtime Ultrasound

Pigs from the 2005 genetic line were leaner (P < 0.01) for all three real-time ultrasounds and had larger (P < 0.01) loin muscle areas for real-time ultrasounds 1 and 2 than pigs from 1980 genetic line. Pigs fed 2005 feeding program had larger (P < 0.01) loin muscle area for all three real-time ultrasound measurements but did not differ for backfat. Data summarized in table 6.

Genetic improvement led to increases in ADG, leanness, muscle and thus improved the overall lean gain. Changes in feeding programs led to improvements in ADG and muscle, reduced ADFI and as a result improved overall lean gain.

Genetic line 1980 2005 G*FP Genetic

line Feeding program

Feeding program 1980 2005 1980 2005 P-value P-value P-value On-test to nursery ADG, kg 0.4795a 0.5338b 0.4349a 0.5435b 0.0975 0.2859 <0.0001 ADFI, kg 0.9356a 0.8877ab 0.811b 0.8301b 0.2893 0.0055 0.6464 Gain:Feed 0.52a 0.60b 0.54a 0.66c 0.1514 0.0011 <0.0001 Nursery to slaughter ADG, kg 0.8526a 0.9121b 0.9255b 1.0419c 0.0495 <0.0001 <0.0001 ADFI, kg 2.6815a 2.3876b 2.5903ac 2.4607bc 0.0908 0.8499 <0.0001 Gain:Feed 0.32a 0.38b 0.36c 0.42d 0.9824 <0.0001 <0.0001 On-test to slaughter ADG, kg 0.7513a 0.7989b 0.7879b 0.8851c 0.0406 <0.0001 <0.0001 ADFI, kg 2.2076a 1.9358b 2.0913c 1.9466b 0.0795 0.1265 <0.0001 Gain:Feed 0.3402a 0.4124b 0.3785c 0.4554d 0.6001 <0.0001 <0.0001 Lean gain, on-test to slaughter Lean ADG, kg 0.2332a 0.2495b 0.2656c 0.3109d 0.0045 <0.0001 <0.0001 ADFI, kg 2.2076a 1.9385b 2.0913c 1.9466b 0.0795 0.1265 <0.0001 Lean gain:feed 0.11ab 0.13a 0.13b 0.16ab 0.0708 <0.0001 <0.0001 Lean gain, first scan to slaughter Lean ADG, kg 0.2483a 0.2503a 0.3128b 0.3341c 0.2146 <0.0001 0.1354 ADFI, kg 2.8826a 2.5464b 2.8328ac 2.6885bc 0.0853 0.4034 <0.0001 Lean gain:feed 0.09a 0.10b 0.11c 0.13d 0.8431 <0.0001 0.0002 Slaughter Slaughter age, d 177.64a 169.16b 170.11b 154.61c 0.0760 <0.0001 <0.0001 Slaughter wt, kg 118.37a 118.94b 119.21c 119.33d 0.7525 0.3856 0.6301

47

47

Table 6. Effect of Genetic Line and Feeding Program on Real-time ultrasound BF and LMA

Genetic line 1980 2005 G*FP Genetic

line Feeding program

Feeding program 1980 2005 1980 2005 P-value P-value P-value BF1, cm 1.18a 1.28a 1.00b 1.03b 0.3931 <0.0001 0.1033 LMA1, cm2 17.99a 19.45b 18.20a 20.38b 0.4196 0.2008 0.0001 BF2, cm 1.79a 1.81a 1.45b 1.51b 0.7772 <0.0001 0.4272 LMA2, cm2 26.20a 28.28b 27.29ab 30.76c 0.1267 0.0003 <0.0001 BF3, cm 2.34a 2.50a 1.93b 2.03b 0.7045 <0.0001 0.1043 LMA3, cm 34.29a 36.39b 36.69b 40.27c 0.2102 <0.0001 <0.0001 a-cLeast squares means with no common superscript differ (P < 0.05)

Carcass Traits

Carcasses were heavier (P < 0.01) but shorter (P < 0.01) for pigs fed 2005 feeding program compared to pigs fed 1980 feeding program. Dressing percent for pigs fed 2005 feeding program was higher (P < 0.05). Pigs fed the 2005 feeding program had thicker (P < 0.05) bellies on the loin side but shorter (P < 0.05) and less firm (P < 0.01) (as measured by the bellystick) bellies than pigs fed 1980 feeding program. 2005 genetic line pigs had firmer (P < 0.05) (as measured by the bellystick) than pigs from 1980 genetic line pigs. All carcass measurements for loin muscle area and backfat were adjusted to 85 kg. Genetic line x feeding program interaction (P < 0.05) where 2005 (15.40%) genetic line pigs and 1980 (8.2%) genetic line pigs had larger loin muscle area when fed 2005 feeding program versus 1980 feeding program. Pigs from 2005 genetic line were leaner at first rib (P < 0.05), 10th rib (P < 0.01) and last lumbar (P < 0.01) than pigs from 1980 genetic line. Pigs fed 1980 feeding program were leaner at first rib (P < 0.01) and last rib (P = 0.05) than pigs fed 2005 feeding program. Table 7. Effect of Genetic Line and Feeding Program on Carcass Measurements Genetic Line 1980 2005 G*FP Genetic

line Feeding program

Feeding program 1980 2005 1980 2005 P-value P-value P-value

Carcass length, cm 87.01a 85.27b 87.07a 85.04b 0.7338 0.8590 0.0004

Dressing % 73.03ab 73.64a 72.4b 73.81a 0.2733 0.5755 0.0225 HCW, kg 84.88a 87.31b 85.42a 88.05b 0.8960 0.4429 0.0062 LMA, cm2 36.84a 39.87b 42.73c 49.31d 0.0355 <0.0001 <0.0001 BF 10th rib, cm 3.19a 3.30a 2.51b 2.41b 0.2486 <0.0001 0.9390

BF 1st rib, cm 3.99ac 4.34b 3.73c 4.09ab 0.9557 0.0356 0.0064

BF last rib, cm 2.89ab 3.05a 2.72b 2.92ab 0.7458 0.0821 0.0535

BF last lumbar, cm 2.56a 2.62a 2.27b 2.45ab 0.2967 0.0013 0.0866 a-cLeast squares means with no common superscript differ (P < 0.05)

48

48

Table 8. Effect of Genetic Line and Feeding Program on Trimmed Belly Measurements Genetic line 1980 2005 G*FP Genetic

line Feeding program

Feeding program

1980

2005

1980

2005

P-value

P-value

P-value

Thickness loin edge, cm 4.01a 4.43b 4.14ab 4.35ab 0.3988 0.8665 0.0429 Thickness teat edge, cm 3.25a 3.53a 3.41a 3.63a 0.8401 0.4117 0.1433 Thickness ham end, cm 3.63a 3.75a 3.66a 3.88a 0.8211 0.7070 0.4641 Thickness shoulder end, cm 4.90a 4.78a 5.24a 4.75a 0.3389 0.4544 0.1708

Stick test, cm 33.66a 20.96b 27.81c 19.63b 0.1281 0.0320 <0.0001

Belly length, cm 53.84a 52.32a 53.52a 51.80a 0.8653 0.5307 0.0285 a-cLeast squares means with no common superscript differ (P < 0.05)

Genetic improvements have increased muscling, leanness and belly firmness. Changes in nutrition have improved muscling and dressing percent but have resulted in shorter carcasses that are fatter and have reduced belly firmness.

Pork Quality

Loin and ham quality data are summarized in Tables 9 and 10. Loins from pigs fed 1980 feeding program had higher (P < 0.01) 45 minute pH. Genetic line x feeding program interactions (P < 0.05) were observed for percentage intramuscular fat and visual marbling scores in the loin muscle where 2005 genetic line pigs fed the 1980 feeding program had the highest visual marbling score and greatest percent intramuscular fat. Both subjective marbling score and percent intramuscular fat for the loin muscle were higher (P < 0.01) for pigs from the 2005 genetic line versus pigs from the 1980 genetic line and pigs fed the 1980 feeding program versus pigs fed the 2005 feeding program. Drip loss percentage measured on loin muscle samples also showed similar results; less (P < 0.05) drip loss was observed for 2005 genetic line pigs and pigs fed the 1980 feeding program. Warner Bratzler shear force measured on cooked pork loin samples was more (P < 0.05) desirable for 1980 genetic line pigs compared to 2005 genetic line pigs. Ham pH measured 24 hour postmortem and ham subjective wetness score were higher (P < 0.05) for pigs fed 1980 versus 2005 feeding program. Hams from 1980 genetic line pigs were more (P < 0.05) red (Minolta a) and more (P < 0.01) blue (Minolta b) than those from 2005 genetic line pigs.

49

49

Table 9. Effect of Genetic Line and Feeding Program on Loin Muscle Quality. Genetic line 1980 2005 G*FP Genetic

line Feeding program

Feeding program

1980

2005

1980

2005

P-value

P-value

P-value

pH45 6.28ac 6.13b 6.28a 6.10bc 0.7657 0.7766 0.0107 pHu 5.69ab 5.66ab 5.74a 5.60b 0.2090 0.9953 0.0924 Marbling, subjective 1.71a 1.44a 2.85b 1.74a 0.0110 0.0002 0.0006 % IMF 0.0420a 0.0300b 0.0625c 0.0342ab 0.0189 0.0019 <0.0001 Firmness, subjective 2.49ab 2.31a 2.65b 2.41ab 0.7978 0.2546 0.0869 Wetness, subjective 2.55a 2.43a 2.57a 2.58a 0.5103 0.4537 0.6906 Color, subjective 2.30ab 2.20a 2.56b 2.38ab 0.7531 0.1447 0.3802 Minolta L 53.22a 52.14a 53.20a 52.42a 0.7673 0.8167 0.1227 Minolta a 9.39ab 9.38ab 10.06a 8.93b 0.0913 0.7512 0.1395 Minolta b 6.45a 6.26a 6.76a 6.24a 0.4734 0.5668 0.1971 Drip loss, % 0.029a 0.035a 0.022b 0.029ab 0.9462 0.0195 0.0347 WBS, kg 2.93a 2.76b 2.90a 3.05a 0.0022 0.0255 0.8099 a-cLeast squares means with no common superscript differ (P < 0.05) Table 10. Effect of Genetic Line and Feeding Program on Ham Quality. Genetic line 1980 2005 G*FP Genetic

line Feeding program

Feeding program

1980

2005

1980

2005

P-value

P-value

P-value

pH45 5.96a 5.89a 5.88a 5.97a 0.1705 0.9772 0.8554 pHu 5.83ab 5.72a 5.89b 5.69a 0.3679 0.7407 0.0144 Wetness, subjective 2.02ab 1.84ba 2.31b 1.62a 0.1010 0.8458 0.0166 Firmness, subjective 2.18a 2.05a 2.41a 2.23a 0.9017 0.3091 0.4788 Color, subjective 2.73a 2.77a 2.90a 2.64a 0.4604 0.9086 0.6484 Minolta L 50.52a 50.33a 49.82a 50.25a 0.6572 0.6158 0.8913 Minolta a 11.03ab 11.54a 10.65b 10.23b 0.1575 0.0226 0.9062 Minolta b 4.13ab 4.61a 3.56b 3.67b 0.4151 0.0051 0.2863 a-cLeast squares means with no common superscript differ (P < 0.05)

Genetic improvement appears to have had favorable affects on intramuscular fat and water holding capacity in the loin muscle. Changes in nutrition have reduced loin muscle intramuscular fat and water holding capacity along with reducing loin and ham pH.

Eating Quality

Ratings from a consumer sensory evaluation and trained flavor and descriptive panels for pork

loin chops are summarized in tables 11 and 12. Loin samples from pigs fed the 1980 feeding program tended to have a higher (P < 0.09) flavor liking and a more (P < 0.08) intense juiciness than loin samples from pigs fed 1980 feeding program. Texture liking was higher (P = 0.05) for samples taken from pigs fed the 1980 versus 2005 feeding program. Overall liking for pork loin samples was higher (P < 0.01) from pigs fed 1980 feeding program than pigs fed 2005 feeding program. The difference between 1980 and 2005 genetic line did not appear to affect any of the eating qualities measured in this consumer panel.

50

50

Table 11. Effect of Genetic Line and Feeding Program on Consumer Sensory Analysis for Pork Loin Chops

a-cLeast squares means with no common superscript differ (P < 0.05)

Cooked pork aroma was stronger (P < 0.01) in loin samples from pig fed the 1980 feeding program. Loin samples from pig fed 2005 feeding program had a higher (P = 0.02) cohesive mass score than samples from pigs fed 1980 feeding program. There was genetic line x feeding program interaction (P < 0.01) for cooked pork flavor where loin samples from 1980 genetic line pigs fed 1980 feeding program versus 2005 feeding program did not differ but loin samples from 2005 genetic line pigs had more cooked pork flavor when fed 2005 feeding program versus 1980 feeding program. Genetic line x feeding program interactions were observed where loin samples from 2005 genetic line pigs did not differ when fed 1980 feeding program versus 2005 feeding program, however loin samples from 1980 genetic line pigs fed 2005 feeding program were juicier (P < 0.01) and had more moisture release (P < 0.01) than loin samples from pigs fed 1980 feeding program. Also genetic line x feeding program interactions were observed where loin samples from 2005 genetic line pigs were harder (P<0.01) and more fibrous (P<0.01) when fed 2005 feeding versus 1980 feeding program while loin samples from 1980 genetic line pigs fed 1980 feeding program versus 2005 feeding program were harder (P < 0.01) and more fibrous (P < 0.05).

Table 12. Effect of Genetic Line and Feeding Program on Trained Flavored and Descriptive Panels for Pork Loin Chops

Genetic line 1980 2005 G*FP Genetic

line Feeding program

Feeding program 1980 2005 1980 2005 P-value P-value P-value

Overall liking 6.05a 5.49b 5.94a 5.65ab 0.4001 0.9195 0.0081

Flavor liking 5.89a 5.46a 5.79a 5.63a 0.4181 0.8364 0.0840

Juice intensity 4.24ab 3.82a 4.39b 4.14ab 0.6624 0.2007 0.0717

Texture liking 5.68a 5.17b 5.76a 5.59ab 0.3414 0.1521 0.0510

Tenderness intensity 4.23ab 4.03a 4.55b 4.21ab 0.6975 0.1723 0.1499

Genetic line 1980 2005 G*FP Genetic

line Feeding program

Feeding program 1980 2005 1980 2005 P-value P-value P-value Cooked pork aroma 3.50a 3.39a 3.67b 3.37a 0.0557 0.1022 <0.0001 Cooked pork flavor 4.77a 4.87ab 4.97b 4.81a 0.0037 0.1080 0.4783 Piggy 0.00a 0.00a 0.00a 0.00a 0.3162 0.3162 0.3162 Metallic 1.54a 1.57a 1.62a 1.61a 0.6347 0.0568 0.7595 Astringent MF 1.53a 1.52a 1.54a 1.53a 0.9774 0.6099 0.5902 Oxidized 0.00a 0.02a 0.00a 0.00a 0.3162 0.3162 0.3162 Sweet 1.57a 1.59a 1.61a 1.56a 0.0743 0.9532 0.4816 Salt 0.48a 0.50a 0.54a 0.48a 0.4237 0.5871 0.6010 Sour 1.78a 1.77a 1.78a 1.84a 0.2687 0.2777 0.4607 Bitter 0.00a 0.01a 0.00a 0.00a 0.3184 0.3184 0.3184 Hardness 7.07a 6.63b 6.62b 7.28a <0.0001 0.2663 0.2462 Moisture RLS 3.65a 4.02b 3.76a 3.67a 0.0021 0.0988 0.0550 Cohess mass 6.51b 6.28a 6.47ab 6.33ab 0.5386 0.9462 0.0200

Juicy 4.10a 4.36b 4.14a 3.99a 0.0018 0.0124 0.4222 Fibrous 5.38a 5.21b 5.21b 5.47a 0.0004 0.4698 0.4894 Oily MCT 0.56a 0.57a 0.59a 0.58a 0.3591 0.2026 0.8755 # of Chews 35.94ab 34.71a 34.98a 37.63b 0.0042 0.1456 0.2873

51

51

a-cLeast squares means with no common superscript differ (P < 0.05)

Changes in genetics do not appear to have affected eating quality traits for pork loin chops either negatively or positively. However, nutritional changes appear to have led to poorer eating quality traits as measured by consumer sensory analysis but not the trained flavor and descriptive panels for pork loin chops. Conclusion Overall the pork industry has made advances in traits of economic interest through genetic selection and improved feeding practices. Improvement in growth rate is due to equal contributions from genetics and feeding programs while observed improvements in efficiency were primarily due to changes in feeding practice. However, improved carcass composition is primarily a result of genetic improvement. Pork quality characteristics have been improved by genetic selection but this did not result in measurable improvements in eating quality. Changes in feeding practices over the last 25 years have had negative impacts on measures of pork quality and consumer preference. These results provide the pork industry a greater understanding of industry changes and provide documentation that can be used with general public and regulatory groups on positive developments that have occurred. In addition, the results of this study provide data that can be used to develop future research studies to investigate the underlying biological mechanisms associated with the observed changes. References American Society for Testing Materials. 1981. Guidelines for the selection and training of sensory panel members. ASTM STP 758. Philadelphia, PA. Cairncross, S.E. and Sjostrom, L. B. 1950. Flavor profiles - a new approach to flavor problems. Food Technol. 4:308-311. Caul, J. F. 1957. The profile method of flavor analysis. Adv. Food. Res. 7:1-40. Cheema, M.A., M.A. Qureshi, and G.B. Havenstein. 2003. A comparison of the immune response of a 2001 commercial broiler with a 1957 randombred strain when fed “typical” 1957 and 2001 broiler diets. Poultry Sci. 82:1519:1529. Havenstein, G.B., P.R. Ferket, and M.A. Qureshi. 2003a. Growth, livability, and feed conversion of 1957 vs 2001 broilers when fed “typical” 1957 and 2001 broiler diets. Poultry Sci. 82:1500-1508. Havenstein, G.B., P.R. Ferket, and M.A. Qureshi. 2003b. Carcass composition and yield of 1957 vs 2001 broilers when fed “typical” 1957 and 2001 broiler diets. Poultry Sci. 82:1500-1508. Havenstein, G.B., P.R. Ferket, S.E. Scheideler, and B.T. Larson. 1994a. Growth, livability, and feed conversion of 1957 vs 1991 broilers when fed “typical” 1957 and 1991 broiler diets. Poultry Sci. 73:1785-1794. Havenstein, G.B., P.R. Ferket, S.E. Scheideler, and D.V. Rives. 1994b. Carcass composition and yield of 1957 vs 1991 broilers when fed “typical” 1957 and 1991 broiler diets. Poultry Sci. 73:1795-1804. National Pork Board. 2005. Quick facts: The pork industry at a glance. National Pork Board, Des Moines, IA #09133. Qureshi, M.A. and G.B. Havenstein. 1994. A comparison of the immune performance of a 1991 commercial broiler with a 1957 randombred strain when fed “typical” 1957 and 1991 broiler diets. Poultry Sci. 73:1805-1812.

52

52

Schwab, C.R., T.J. Baas, K.J. Stalder, and J.W. Mabry. 2006. Effect of long-term selection for increasedleanness on meat and eating quality traits in Duroc swine. J. Anim. Sci. 84: 1577-1583. Stalder, K.J., C. Johnson, D.P. Miller, T.J. Baas, N. Berry, A.E. Christian, and T.V. Serenius. 2005. Pocket guide for the evaluation of structural, feet, leg, and reproductive soundness in replacement gilts. National Pork Board, Des Moines, IA. #04764.

53

53

Sequencing the Swine Genome: Progress and Prospects

Max F. Rothschild C.F. Curtiss Distinguished Professor

Department of Animal Science Center of Integrated Animal Genomics

Iowa State University mfrothsc@iastate.edu

Introduction The recent near completion of the human genome sequence in the beginning of 2001 has catapulted our understanding of our genetic complexity as human beings. Furthermore, mining this wealth of information will allow biologists to understand human diversity including traits like height and weight or eye and hair color and even more complex traits like susceptibility to various diseases. This means that in the next 10-20 years a whole new form of medicine, called genomic medicine, will make it possible to develop individualized diagnoses, treatments and cures for each person based on their individual and unique genotype. That will revolutionize medicine. Around the world scientists are spending billions of dollars to learn more about the human genome and these results may be used to better understand pig health, reproduction, growth, and behavior by comparing the pig genome sequence to the human genome sequence. To date, we now have the ability to select animals on the basis of individual gene tests for improved reproductive performance, growth rate, leanness and meat quality. Already this has meant millions of dollars of improvement in several of these traits. But imagine for the moment, using not just 5 or 10 genes to select for a trait but 100s or 1000s of genes to improve pig production and create specialized pork products. To do this, sequencing of the pig genome is required, and this will revolutionize pork production. What is sequencing? Sequencing is the unraveling of the DNA to understand the genetic code (Figure 1). It is equivalent to breaking down books into individual sentences and even specific letters in these sentences and words (Figure 2). The letters in the genetic code (A, T, G, C) are combined into “words” and these words are the genes that control traits or contribute to phenotypes of the animal like rate of growth, level of fat, reproductive performance and disease susceptibility. Knowing the genetic code requires that we apply modern molecular biology or laboratory methods to break up the code into smaller pieces and then “read” the code. Funding to sequence the pig genome is an international effort provided by the USDA, National Pork Board, Iowa Pork Producers Association, University of Illinois, Iowa State University, North Carolina Pork Council, North Carolina State University, the Wellcome Trust Sanger Institute, UK and a number of research institutions from around the world including those from China, Denmark, France, Japan, Korea, Scotland and the U.K. Figure 1. Unraveling of chromosomal information to the individual genes. Figure adapted from DOE human genome figure.

54

54

Figure 2. Comparison of ordered sequencing (left side) and shotgun sequencing (right side). This figure is from DOE human genome project. Sequencing from both methods leads to final sequence.

55

55

Progress of the sequencing efforts The pig genome sequencing began in part when a Danish-Chinese project was initiated several years ago. This project produced a 0.6 X coverage. To have excellent sequence, a 6X copy of sequence is needed. The new effort initiated recently by US, UK and other country partners has as its goal a 3X -4X coverage with additional sequencing coverage being obtained from foreign lab contributions. Already this new effort is progressing nicely. Updates can be seen daily at http://www.animalgenome.org/pigs/genomesequence/. These updates are provided as part of the USDA Bioinformatic Coordinator's team effort. Other information about the sequencing can be seen at that page and web pages at the Sanger Institute and the University of Illinois (see http://www.piggenome.org/sequence.php). Additional details about the sequencing efforts can be read from the Pig Genome Update also at http://www.animalgenome.org/pigs/newsletter/index.html or at the International Genome Consortium Sequencing Newsletter (http://www.animalgenome.org/pigs/newsletter/index.html ). Figure 3. Update of sequencing efforts at a glance see http://www.animalgenome.org/pigs/newsletter/index.html (example November 11, 2006) Chromosome 17: an example in progress So how in fact does the sequencing really help? At present we have good but not complete maps of the pig genome. The sequencing will provide not only the “ultimate genetic map” but will allow us to have the tools to hunt down mutations of interest in our own specialized herds and families. This genome sequence of the pig serves as a template to look into the sequence differences in pigs of interest for traits that are economically important. An example of this effort is that of our lab on chromosome 17. A genome scan, performed using an F2 population derived from a Berkshire x Yorkshire cross, identified several meat quality QTL on pig chromosome 17 (SSC17) (Malek et al., 2001).These QTL included three meat color traits (color, 48 hour loin Hunter L value and 48 hour loin Minolta L value) and two lactate related traits (average lactate and average glycolytic potential). The identification of the mutations responsible for these QTL has proved to be a very challenging task, but it is necessary to allow the utilization of these QTL in modern pig breeding schemes. The objective of this study, part of a PhD project of A. M. Ramos, was to first more clearly

Chromosome Progress 20/11/06

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 X

% c

over

age

of c

hrom

osom

es

Committed

Unfinished

Finished /Phase3

11/6/06

56

56

identify the chromosomal region(s) more likely to contain the causative mutation(s) responsible for the observed meat quality QTL and secondly to find the mutations themselves (Ramos et al., 2007). A total of 19 genes and 6 anonymous markers were first mapped to SSC17 after the initial scan. The developing linkage map then contained 33 genetic markers and was 119.3 cM long. The SSC17 marker density was substantially increased, especially under the QTL peaks, where the distance between markers was always less than 3 cM. From this information it was clear that progress could only then be made using the full pig genome sequence. Funding provided in part by Iowa State University and the Iowa Pork Producers Association was then used to obtain the finished sequence directly in the region under the QTL. Previously, a physical map of overlapping pieces (called BAC ends) of the swine genome was developed by members of the Swine Genome Sequencing Consortium. A BAC tiling path (large overlapping pieces) spanning the SSC17 QTL region was selected and sequenced at the Sanger Institute. These clone sequences were aligned in order to generate a consensus sequence. The final alignment contained 65 clones and was approximately 7.8 Mbp long (paper in preparation). Initial annotation done on the pig sequence confirmed the extensive conservation between SSC17 and HSA20 (all human genes were found in the pig sequence and all genes maintained their human order in the pig sequence). Detailed manual annotation to Human Genome Havana standards is nearly completed at the Sanger Institute. The sequence was then used to develop Berkshire and Yorkshire specific sequence and a total of over 25 SNPs were found, mostly in the coding regions of the many genes underlying the QTL graph. To date at least three and possibly four of these SNPs are in close association with the traits of interest. It is clear that this is a region, specific to Berkshire containing many favorable alleles. Further work is continuing and is likely to produce results in the near future. Without the sequencing, real efforts to find these genes would be futile. The benefits pork producers might see Sequencing the swine genome is an investment in basic research with both long- and short-term goals. The potential usefulness of genes in selection for improved pig performance will be determined more quickly if the pig genome sequence is available. Discovery and elimination of undesirable forms or alleles of these genes will be accelerated. Past examples include the removal of mutant or negative alleles of the stress gene (HAL) and Rendement Napole (RN) gene. In the last 10 years several genes have been identified which improve performance and leanness (IGF2, MC4R), meat quality (CAST, PRKAG3) and reproduction (ESR, PRLR). Sequencing of the pig genome offers the ability to multiply these discoveries into the 1000s and speed the rate of these discoveries. Greater federal funding for pig genomic research can be leveraged to provide more rapid application in these areas. For the average pork producer the many benefits include improved growth and litter size performance due to identification of genes affecting these traits. The genome sequence is a powerful tool, which will enable discoveries for improving traits of interest for producers regardless of their operational size, but those producers and companies associated with more advanced research groups or breeding companies may have the opportunity to leap frog with new genomic strategies. For these better positioned producers and early adopters more advanced opportunities are likely to include in the next 5-20 years the ability to produce pigs with improved immune response abilities (vaccine ready pigs), growth primed sire lines and development of increased niche and branded products representing unique or special attributes that one producer or one company wishes to use to increase market share and profits. It is likely that producers will have the ability to select certain genetic lines in the future that will require specialized feeds but that will outperform existing lines by 20 to 40 %. Given that our competitors in the chicken and beef industries already have the chicken and cattle genomes sequenced we must move forward if we are to be competitive. The pig genome sequence will be essential for identifying specific genes and improving those traits that are difficult to measure, occur late in life or are evaluated on animals after harvest such as disease resistance, sow longevity and meat quality. Already we know of specific genes associated with sturdier sows and improved meat quality. Insights may be gained into how genes work together. This will allow better genetic planning to allow pig

57

57

breeders and producers to select animals possessing certain sets of genes that interact in a favorable manner for a particular production system or niche market. This approach, termed “genomic selection,” will mean not just selecting on improved traits but selection on 1000s of genes. Conclusions Sequencing efforts have started and are moving along nicely. Results of these efforts are already being used to help select markers for improved growth and meat quality. Given the funding available, about $15 million presently, it is likely we will have a draft sequence of the pig genome by late 2007 or early 2008. Will companies and seedstock breeders be ready to take advantage of these discoveries? Producers must ask the difficult questions. Are they ready to use the new genetics and genomics information? Are they positioned to first understand the information and second to use it effectively? Are there genetic systems in which they can use this information more effectively to improve pig production? Do they have niche markets they wish to fill or new products to produce? Team work and partnerships with the right seedstock breeders or breeding companies and university research faculty are likely to be keys in transforming this public information from a useful resource to a real payoff. Only then will producers, companies and geneticists help members of the pig industry really bring home the bacon. Acknowledgements: Collaborative research efforts of Drs. G. Plastow, A. Mileham and members of the Rothschild lab and those of Dr. A. M. Marcos are appreciated. A number of researchers world wide have participated in the sequencing efforts and in particular Dr. Larry Schook, UI and Jane Rodgers and Sean Humphray, Sanger Center are noted. The author wishes to thank financial support received from the USDA NRSP8 which supports the National Pig Genome Coordination project. Support for the pig genome sequencing and that of the chromosome 17 sequencing efforts comes from the USDA, National Pork Board, Iowa Pork Producers Association, University of Illinois, Iowa State University, North Carolina Pork Council, North Carolina State University, the Wellcome Trust Sanger Institute, UK and a number of research institutions from around the world including those from China, Denmark, France, Japan, Korea, Scotland and the U.K. Funding for individual research has been provided in part by Sygen and PIC USA and by Hatch, Iowa Agricultural Experiment Station and State of Iowa funds. References Hu, J. Sean Humphray, Carol Scott, Jane Rogers, Antonio Marcos Ramos, James M. Reecy and Max F. Rothschild. 2006. Regional Genome Sequence Assembly for a Targeted Segment on Porcine Chromosome 17. Proc. Plant and Animal Genome XIV, San Diego, CA, Jan. 14-18. Malek, M., J. C. M. Dekkers, H. K. Lee, T. J. Baas, K. Prusa, E. Huff-Lonergan, and M. F. Rothschild. 2001. A molecular genome scan analysis to identify chromosomal regions influencing economic traits in the pig. II. Meat and muscle composition. Mammal. Genome 12:630-636. Ramos, A.M., Z.L. Hu, S J. Humphray, J. Rogers, J. Reecy and M. F. Rothschild. 2007. Combining fine mapping and genome sequencing for the molecular dissection of pig ssc17 meat quality QTL. Genetics (submitted)

58

58

59

59

Candidate Genes Associated with Sow Longevity

B.E. Mote, K.J. Stalder, and M.F. Rothschild

Department of Animal Science and Center for Integrated Animal Genomics, Iowa State University, Ames, Iowa.

Introduction The economic efficiency of swine operations is always a topic of discussion for pork producers and allied industries especially now when feed grain prices are being pushed higher as the demand for bio-fuels increases. However, many factors outside of feed prices influence the breakeven costs and thus economic efficiency for swine operations. Of those additional factors, sow herd performance is typically one of the most important categories. When producers talk about sow herd performance, most producers think about farrowing rate, pigs weaned per litter, or generally the number of pigs weaned per sow per year. We suggest that when talking about sow herd performance we need to take a more holistic view of the sow herd and incorporate sow longevity, more accurately called sow productive life (SPL) into the profit equation. The growing percentage of sows leaving the farm before they recuperate there investment cost has been increasing as of late. These sows are being involuntarily removed from the farm for reasons such as reproductive failure, locomotion failure, and death. This early removal or premature death increases sow replacement rates and has both economic and welfare consequences to the swine industry. PigCHAMPTM records from 1998 through 2005 show an increasing trend in the death rate from 5.9% in 1998 to 8.94% in 2005 (see figure 1). The same records show that the culling rate is more variable but was at an unprecedented 51% in the most recent data available (see figure 2) (PigCHAMPTM, 2006). The high culling rate seen in 2005 could be generated in part by producers taking advantage of profits and restocking their herds. High replacement rates driven by involuntary culling infer that producers are required to lower their selection intensity to maintain herd size. High replacement rates can cause a downward spiral in herd performance in systems with undersized multiplication efforts, since a heavy demand for replacement gilts may result in sub-standard gilts or gilts not properly developed entering the breeding herd. Improving SPL would help alleviate the pressures placed on multiplication herds allow for gilts to be selected more on quality than simply on quantity. Using standard net present value calculations for a farrow to finish operation such as a purchase price of $200 per gilt, an average number born alive/litter of 10.2, 8.5 pigs sold per litter, and an average price of 44 $/CWT for market hogs, an increase in net present value of $77.38 per sow could be realized if an operation could increase litters per sow from three to four (Stalder et al., 2000). Thus an increase in average parity of just one tenth would increase the profit by $0.23 for every market hog sold from the operation. For a farrow to wean operation, using the same purchase price, number born alive/litter with an average price per head of $28 for segregated early weaned (SEW) pigs, and marketing 9 pigs per litter, the net present value per sow would increase by $45.59 if a sow would have four parities instead of three (Stalder et al., 2003). An increase in the average parity of one tenth would increase the profit of a farrow to wean operation by $0.13 per pig sold. Taken as a whole, a one tenth increase in average parity for the herd would raise the profit by approximately $15,000,000 per year in the U.S. alone. Limited studies have been performed researching productive life in pigs. Most studies were only conducted up to either sow parity three (Rozeboom et al., 1996) or four (Moeller et al., 2004) allowing for some understanding as to why sows leave the herd in early parities, but never accounting for reasons why other sows can thrive well beyond four parities. These previous studies revealed significant line interactions on sow longevity and noted that further studies should be conducted to identify the genetic mechanisms associated with sows having increased numbers of parities. Scientists have begun identifying genes in model organisms that play a role in the aging process and longevity itself (Hasty et al., 2003; Hekimi and Guarente, 2003; Longo and Finch, 2003; Simon et al., 2003; and Tatar et al., 2003). Research has shown that yeast and. C. elegans (nematode) share a number of homologous genes in the so called “longevity pathways” and that increased longevity is often the result of inactivation of the

60

60

pathways that promote growth and a reduction in oxidative damage and other forms of stress (Longo and Finch, 2003). Similar results have also been shown in the fruit fly such as mutations in the insulin / IGF-1 pathways extending lifespan. The overriding theme gathered from studying these genes is their role in reduction of caloric intake that enables animals to live longer as well as reducing susceptibility to disease in the aging process. However, some research has indicated that leaner gilts have the tendency to be removed from the herd earlier (Stalder et al., 2005). The hypothesis that guides this comparative genomics research is that the similarity between the functions of certain genes in the various species studied suggests that the same genes may be associated with SPL in the pig. It is possible that genes associated with increasing simple lifespan in model organisms might not be correlated with SPL since it is more than a measure of longevity. It is also plausible that the non lean allele could be more beneficial to SPL as sows with more backfat have shown the tendency for having a longer SPL or remain in the breeding herd for a longer period of time. Additionally, other genes more specific to swine may need to be isolated and examined. Genes studied include those that function as antioxidants, are involved in reproduction, and are components of the insulin pathway that regulate food intake. The identification of molecular markers associated with the length of a sow’s productive life would allow breeders to use marker assisted selection to select individuals, based on the animal’s genotype, at early ages that would have the best opportunity to remain in the herd far beyond the current average sow. Animal Populations

We have used several distinctly different populations throughout this study. The first population that was analyzed consisted of approximately 1000 commercial sows where half were younger than parity four and the remaining were parity six or greater. The only phenotypic data collected on these animals was the number of parities each sow generated. The second population consisted of approximately 200 sires. The information collected and used in the analysis of this population was the EBVs based on phenotypes from a minimum of ten daughters per sire. The third population consisted of commercial females from varying parities and was primarily used to evaluate reproduction performance and thus contained reproductive data. The previous three populations were primarily mid 1990s genetics and no one population contained all the phenotypic records necessary to completely evaluate the candidate genes for SPL. Therefore, we felt it necessary to sample current genetics where all phenotypic records were collected from one population which would enable accurate analysis of the current state of commercial sow industry. The fourth population used here to validate the earlier results of Mote et al., 2005, consisted of 2,000 commercial crossbred females composed of two parent lines where heterosis is maximized. Commercial crossbred females were selected for this study, since these females are the objective of an effective breeding program seeking to improve maternal line traits and are where traits like SPL should be evaluated. Equal numbers of sows were randomly sampled from each of the two crossbred lines utilized in this study. The experimental sows were randomly sampled from three farms that contained a total of 11,400 sows in their production system. Half (1000 animals) of the sampled sows have had greater than 5 parities and serve as the selected group and the remaining half (1000 animals) are replacement gilts and served as the unselected group. Equal numbers of selected and unselected females were sampled from each of three farms. Two of the farms utilized one sow commercial line (Line A) and the other farm utilized a second commercial line (Line B). At the time of the last data sampling (June 1, 2006), 201 of the young females had already been removed from the herd with 75 females failing to produce 1 litter, 112 failing after their first litter, and 14 failing after their second litter (at this time point, not all females had sufficient time to produce their second litter).

Data Collection Ear tissue was sampled from all sows using the TypiFixTM ear tag from Agrobiogen. This system allows simultaneous identification and tissue collection to prevent sample misidentification. DNA was isolated from tissue samples using the NexttecTM DNA isolation system (Nexttec GmbH Biotechnologie) adhering to the manufacture’s protocol. PigCHAMPTM records were obtained for all sows at time of tissue collection. The litter records will be resampled approximately every six months so that gilts in the unselected group have sufficient time to farrow additional litters and / or be culled from the breeding herd.

61

61

Statistical analysis Sows’ genotypes were analyzed using Fisher’s exact test to identify if there was a significant deviation in frequency for the gene markers between the select and unselected sow groups for sows remaining in the herd until the fifth parity. Contrast statements were used to identify the differences between genotypes and to determine if the gene has an additive or dominant effect. The PROC MIXED procedure of SAS was used to determine genotype effects on the total number of pigs born alive for the sows using line, farm, group (select vs. unselect) or number of parities, and the sow’s genotype as fixed effects. Additive and dominance effects were estimated for the total number of pigs born alive. Results and Discussion To date, nineteen genes have been studied throughout this research project. At the onset of this project, gene markers were first tested for association in populations 1 and 2 before they were allowed to be tested in populations 3 and 4 for genes believed to be involved in reproduction or in population 4 only if the gene marker did not show association for reproduction but did show association for other SPL traits. Eight genes showed no association with any SPL trait and have been dropped from the study. Seven genes have been tested for association in all pertinent populations while complete analysis has yet to be completed on four gene markers. The seven gene markers that have been fully analyzed in all pertinent populations are: insulin-like growth factor binding protein 1 (IGFBP1), insulin-like growth factor binding protein 2 (IGFBP2), insulin-like growth factor binding protein 3 (IGFBP3), insulin-like growth factor binding protein 5 (IGFBP5), carnitine O-palmitoyltransferase I (CPT1A), organic cation/carnitine transporter 2 (Solute carrier family 22 member 5; SLC22A5), and cyclooxygenase-2 (COX2). These genes were targeted due to their role in the insulin/IGF-1 pathways which has been implicated in model organisms for regulating feed intake and increasing lifespan, their known function in reproduction, or because they are implicated in both roles. The results for Fisher’s exact test concluded that there were significant genotype differences (P < .01) for the gene markers of IGFBP1 (figure 3), IGFBP3 (figure 4), CPT1A (figure 5), and SLC22A5 (figure 6) between the select and unselected groups indicating that there are significant genotypic effects for remaining in the herd until the fifth parity. Additionally for IGFBP1, the same genotype favored for greater longevity also had a significant association with the total number of pigs born alive over the sow’s lifetime (P < .04) (figure 7). Therefore a sow with the beneficial genotype for IGFBP1 will not only have a greater probability of staying in the herd until parity 5, but will also produce an additional 1.5 pigs while doing so. The frequency for IGFBP1 was 0.3 for the 11 genotype and 0.18 for the 22 genotype (favored genotype) in the select group and 0.36 for the 11 genotype and 0.14 for the 22 genotype in the unselected group which also demonstrates that selection is possible on this gene marker. CPT1A was significantly associated (P < .05) with the number of pigs born alive (NBA) but only after the third parity with effects as large as 0.7 of a live pig per litter. The favored genotype for pigs born alive is also the favored genotype for sows remaining in the herd until the fifth parity. IGFBP2 showed a significant association with NBA (figure 8). The genotype that is favored for remaining in the herd shows larger litters in the first two parities with no significant advantage in the third parity, but is then the unfavorable genotype from parity four on. Production goals for the pig production company and genetic company must be evaluated before the use of this genetic marker is implemented into marker-assisted selection (MAS). Sampling of pigs was from large synthetic lines, so it is unlikely that a founder effect exists but it cannot be completely excluded as a cause for the differences in genotypic frequencies between the select and unselected group. In addition, because sampling consisted of animals from two distinct lines, from three farms, and because a large number of sires are used in traditional multiplication systems, a discrepancy in genotypic frequency caused by a founder effect should be minimized if it exists at all. Conclusions Genetic markers associated with components of SPL have been demonstrated using parent animals recently sampled from commercial operations directly addressing the problem of SPL seen in the swine industry today. Four genes (of the completely analyzed 15 genes) (IGFBP1, IGFBP3, CPT1A, and

62

62

SLC22A5) showed significant association with a sows ability to remain in the herd until her fifth parity. Three genes (IGFBP1, IGFBP2, and CPT1A) showed significant associations with reproductive traits during the sow’s productive lifetime. All significant genes have allele frequencies that are favorable for use in MAS. These results are evidence that there are genes causing variation in sow productive life and give promise to the use of marker assisted selection to improve sow productive life. ACKNOWLEDGMENTS The authors thank the members of the Rothschild lab, James Koltes, Terry Wolters and Dan Nesvold for their assistance on this project. This work was funded largely by the National Pork Board and in part by USDA National Needs Fellowship, PIC USA, the Iowa Agriculture and Home Economics Experiment Station, State of Iowa and Hatch funding. References Hasty, P., J. Campisi, J. Hoeijmakers, H. van Steeg, and J. Vijg. (2003) Science 299: 1355-

1359.

Hekimi, S. and L. Guarente. (2003) Science 299: 1351-1354. Longo, V. and C. Finch. (2003) Science 299: 1342-1345. Moeller, S. J., R. N. Goodwin, R. K. Johnson, J. W. Mabry, T. J. Baas and O. W. Robison.

(2004) J. Anim. Sci. 82:41-53.

Mote, B., N. Deeb, O. Southwood, and M. Rothschild. AASV Conference. March, 2005. PigCHAMPTM. http://www.pigchampinc.com/benchmarking.asp. Accessed November 28, 2006. Rozeboom, D. W., J. E. Pettigrew, R. L. Moser, S. G. Cornelius, and S. M. El Kandelgy.

(1996) J. Anim. Sci. 74:138–150.

Simon, A.F., C. Shih, A. Mack and S. Benzer. (2003) Science 299: 1407-1410. Stalder, K. J., R. C. Lacey, T. L. Cross, G. E. Conatser, and C. S. Darroch. (2000) Professional Animal

Scientist ;16:33-40. Stalder, K. J., R. C. Lacy, T. L. Cross, and G. E. Conaster. (2003) J Swine Health

Prod. 11:69-74.

Stalder, K. J., A.M. Saxton, G. E. Conatser, and T. V. Serenius. (2005) Livest. Prod. Sci 97:151-159.

Tatar, M. A. Bartke, and A. Antebi. (2003) Science 299: 1346-1351.

63

63

Death Rates

0

2

4

6

8

10

1998 2000 2002 2004

Year

Perc

ent

Figure 1. This figure shows the increasing death rates in commercial swine operations using PigCHAMPTM since 1998.

Culling Rate

30

35

40

45

50

55

60

1998 2000 2002 2004

Year

Perc

enta

ge

Figure 2. This figure shows the variable culling rate in commercial swine units since 1998.

64

64

Figure 3. This figure shows the genotypic frequencies for IGFBP1 of the sows that had 5+ parities (superior), the gilts

(young), and the gilts that have been removed from the herd (fail). The trend lines are to illustrate the decreasing frequency of the 22 genotype in the young and fail groups.

Figure 4. This figure shows the genotypic frequencies for IGFBP3 of the sows that had 5+ parities (superior), the gilts

(young), and the gilts that have been removed from the herd (fail). The trend lines are to illustrate the decreasing frequency of the 22 genotype in the young and fail groups.

0

0.1

0.2

0.3

0.4

0.5

0.6

SUPERIOR YOUNG FAIL

11 12 22

0

0.1

0.2

0.3

0.4

0.5

0.6

SUPERIOR YOUNG FAIL

11 12 22

0

0.1

0.2

0.3

0.4

0.5

0.6

SUPERIOR YOUNG FAIL

11 12 22

65

65

Figure 5. This figure shows the genotypic frequencies for CPT1A of the sows that had 5+ parities (superior), the gilts (young), and the gilts that have been removed from the herd (fail). The trend lines are to illustrate the decreasing frequency

of the 22 genotype in the young and fail groups.

Figure 6. This figure shows the genotypic frequencies for SLC22A5 of the sows that had 5+ parities (superior), the gilts

(young), and the gilts that have been removed from the herd (fail). The trend lines are to illustrate the decreasing frequency of the 11 genotype in the young and fail groups.

0

0.1

0.2

0.3

0.4

0.5

0.6

SUPERIOR YOUNG FAIL

11 12 22

0

0.1

0.2

0.3

0.4

0.5

0.6

SUPERIOR YOUNG FAIL

11 12 22

0

0.1

0.2

0.3

0.4

0.5

0.6

SUPERIOR YOUNG FAIL

11 12 22

0

0.1

0.2

0.3

0.4

0.5

0.6

SUPERIOR YOUNG FAIL

11 12 22

66

66

Figure 7. This figure shows LS Means for the three genotype classes of IGFBP1 for both the number born alive (NBA) and

the total pigs born.

Figure 8. This figure shows LS Means for the three genotype classes of IGFBP2 for both the number born alive (NBA) and

the total pigs born.

757779818385878991

11 12 22Genotype

Num

ber

of p

igs

NBA (P = 0.09) Total Born (P = 0.02)

ababaa

cc

bb

ddcdcd

757779818385878991

11 12 22Genotype

Num

ber

of p

igs

NBA (P = 0.09) Total Born (P = 0.02)

ababaa

cc

bb

ddcdcd

757779818385878991

11 12 22Genotype

Num

ber

of p

igs

NBA (P = 0.001) Total Born (P = 0.001)cc ccdd

bbaa

bb

757779818385878991

11 12 22Genotype

Num

ber

of p

igs

NBA (P = 0.001) Total Born (P = 0.001)cc ccdd

bbaa

bb

67

67

Feeding behavior traits and its impact on economically important traits

JP Cassady†, JM Young†, C Lanier†, MT See†, DS Casey‡

†Department of Animal Science, North Carolina State University, Raleigh, NC ‡PIC, Hendersonville, TN

INTRODUCTION New approaches for improving production efficiency and improving animal well-being are

especially desirable. A greater understanding of the relationship between pig feeding behavior and economically important traits could lead to advances in both production efficiency and well-being. It is important to determine if average daily feed intake provides all the information needed regarding feeding behavior or is the manner in which feed is consumed also important. We know that pigs with identical average daily feed intakes sometime perform differently. This can easily be shown by comparing pigs with identical feed intakes and different growth rates. This is most often observed as differences in feed conversion rate. Could the manner in which pigs consume feed be partially responsible for observed differences in feed conversion rates? What about pigs which eat similar amounts of feed and grow at similar rates but differ in body composition? Could feeding behavior be a contributing factor to those differences? The project reported herein is our initial attempt to address these questions.

Differences in feeding behavior based on breed (Labroue et al., 1994), sex (de Haer and de Vries, 1993) and group size (Hyun and Ellis, 2002) have been studied. A pig can have only one “feeding behavior”. However, several indicator traits which partially describe that feeding behavior can be measured. Indicators of feeding behavior that have been evaluated include number of visits/meals per day, occupation time per visit/meal, occupation time per day, feeding rate per visit/meal, feed intake per visit/meal and average daily feed intake (de Haer et al., 1993). Prior to the development of electronic feeders, measuring individual feed intake required individual housing and was expensive and labor intensive (Nielsen et al., 1995). The development of computerized food intake recording systems allowed for recording of individual feeding behavior while housing pigs in groups through the use of electronic transponders (Young and Lawrence, 1994).

Previous studies which looked at purebred pigs have focused on maternal breeds such as Landrace, Yorkshire and Large White (de Haer et al., 1993; Labroue et al., 1994). The purebred pigs in this study are from lines selected for paternal traits. Although research has been done on feeding behavior in crossbred pigs (Hyun and Ellis, 2001, 2002), it has not been compared to feeding behavior in purebred pigs.

MATERIALS AND METHODS Experimental Design

Data on 8601 purebred boars from 4 sire lines (A, B, C, and D) were collected. Boars were on-test for 13 wk and off-tested from May 10 to November 8, 2004. Boars were housed 15 pigs per pen on two farms. Contemporary group was defined as those boars that were off-tested at the same time from the same farm. Data were collected on 1728 crossbred gilts and barrows, representing three sire lines (A, B, and C) and two dam lines (E and F), from two replicates. Pigs were housed 18 pigs per pen with eight pens per finishing room. Replicate 1 ran from February 26 to August 9, 2004 and replicate 2 ran from July 1 to December 13, 2004. Pigs went on-test at an average weight of 38 kg and were off-tested at an average weight of 114 kg. Contemporary group was defined as those pigs in the same replicate and finishing room.

Data were collected using Feed Intake Recording Equipment (FIRE; Osborne Industries Inc., Osborne, KY) electronic feeders which record ear transponder, entrance time, start weight of feed, exit time, and end weight of feed. Each feeder was used for two pens with a switch gate controlling access to the feeder weekly. On alternating weeks, a conventional feeder was used. Seven behavior traits were derived from feeder data and are average daily feed intake (ADFI), average number of visits per day (ANV), average feed intake per visit (AFIV), average feeding rate per visit (AFRV), average occupation time per day (AOTD), average occupation time per visit (AOTV), and consistency of average daily feed intake (CADFI). The CADFI was a measure to determine if pigs ate approximately the same amount of feed during each 24 hour period and was calculated by fitting a feed intake curve to each pig and then

68

68

averaging the absolute values of the difference between actual feed intake and predicted feed intake. Feeding behavior traits were adjusted for initial weight and feeder prior to analysis.

Performance traits for purebred data are average daily gain (ADG), backfat (BF), loin depth (LD), feed conversion ratio (FCR), and ADFI. Crossbred pigs were individually weighed at birth, weaning, 42 days in nursery, on-test, and every thirty days during test period. Ultrasound backfat thickness and loin depth were measured on-test and every thirty days during test period. Performance traits for crossbred data are ADG, change in backfat over the test period (ΔBF), change in loin depth over the test period (ΔLD), FCR, and ADFI. The ADFI is a feeding behavior trait as well as a performance trait in this study.

LS means were calculated for performance and feeding behavior traits by sire x dam line, sire line, and dam line for crossbred data and by sire line for purebred data using the PROC GLM procedure in SAS (SAS Inst., Inc., Cary, NC). The model used accounted for contemporary group, line, sex (crossbred only), initial weight of pig, and line x sex interaction (crossbred only).

RESULTS AND DISCUSSIONS

LS means for performance and feeding behavior traits are reported in tables 1, 2, and 3. Line B

stands out for feeding behavior traits in both crossbred and purebred data sets. Line B pigs have more but shorter visits to the feeder, eat less per visit, eat at a slower rate, spend more time in the feeder per day, and are more consistent eaters than pigs from Lines A and C. Lines A and C did not differ (P > 0.05) for feeding behavior traits. Line A grows slower and has smaller loins, on average, than lines B and C with Line C having the largest loins, on average.

Table 1. LS means for crossbred pigs by line1

Line2 Trait3 AE AF BE BF CE CF SEM FCR 2.43ab 2.48ac 2.39bd 2.50c 2.34d 2.43b 0.035 ADG 0.869ab 0.856a 0.894c 0.872b 0.890c 0.888c 0.011 ΔLD 20.5ab 19.9a 21.7c 21.2bc 23.1d 22.0c 0.720 ΔBF 7.98a 9.41b 8.19a 9.18b 8.47a 9.49b 0.454 ADFI 2.11ab 2.12ac 2.14ad 2.16d 2.08b 2.16cd 0.032 AOTD 3981ab 4103ac 4239cd 4251d 3919b 4170cd 104 AOTV 888a 857ab 851ac 811c 844ac 844bc 28.4 AFRV 33.8ab 33.8ab 32.9a 33.2a 35.0b 34.4b 0.911 AFIV 489a 470ab 449bc 436c 477ad 463bd 15.1 ANV 4.77a 4.98ab 5.28cd 5.51c 4.87a 5.20bd 0.180 CADFI 0.198ab 0.193a 0.191ac 0.183c 0.207b 0.196a 0.0068 1 Values within a row without a common superscript differ (P<0.05). 2 Line as sire line (A, B, C) x dam line (E, F). 3 Traits: FCR = feed conversion ratio (feed/gain); ADG = average daily gain (kg/day); ΔLD = change in loin depth over test period (mm); ΔBF = change in backfat over test period (mm); ADFI = average daily feed intake (kg/day); AOTD = average occupation time per day (s/day); AOTV = average occupation time per visit (s/visit); AFRV = average feeding rate per visit (g/min); AFIV = average feed intake per visit (g/visit); ANV = average number of visits per day; CADFI = residual daily feed intake (kg/day).

69

69

Table 2. LS means for crossbred pigs by sire line and by dam line1

Sire line Dam line Trait2 A B C SEM E F SEM FCR 2.46a 2.45a 2.40b 0.026 2.38a 2.47b 0.022 ADG 0.862a 0.882b 0.889b 0.008 0.885a 0.873b 0.007 ΔLD 20.1a 21.4b 22.5c 0.52 21.8a 21.0b 0.44 ΔBF 8.82a 8.75a 9.06a 0.33 8.22a 9.37b 0.28 ADFI 2.12a 2.15a 2.13a 0.023 2.11a 2.15b 0.020 AOTD 4054a 4246b 4064a 75.7 4041a 4176b 63.9 AOTV 870a 828b 844ab 20.7 859a 838a 17.4 AFRV 33.8ab 33.0a 34.6b 0.66 33.9a 33.8a 0.56 AFIV 477a 442b 469a 11.0 471a 456b 9.3 ANV 4.90a 5.41b 5.06a 0.13 4.97a 5.23b 0.11 CADFI 0.195a 0.187b 0.201a 0.005 0.199a 0.191b 0.004 1 Values within a row and comparison without a common superscript differ (P<0.05). 2 Traits: FCR = feed conversion ratio (feed/gain); ADG = average daily gain (kg/day); ΔLD = change in loin depth over test period (mm); ΔBF = change in backfat over test period (mm); ADFI = average daily feed intake (kg/day); AOTD = average occupation time per day (s/day); AOTV = average occupation time per visit (s/visit); AFRV = average feeding rate per visit (g/min); AFIV = average feed intake per visit (g/visit); ANV = average number of visits; CADFI = residual daily feed intake (kg/day). Table 3. LS means for purebred pigs by line1

Line Trait2 A B C D SEM FCR 2.33a 2.21b 2.22c 2.26d 0.007 ADG 0.962a 1.009b 1.050c 1.005b 0.003 LD 62.8a 67.0b 73.9c 71.3d 0.19 BF 10.6a 11.1b 11.7c 11.0b 0.07 ADFI 2.22a 2.22a 2.32b 2.26c 0.007 AOTD 4212a 4460bc 4416b 4484c 28.8 AOTV 824a 744b 825a 849c 7.4 AFRV 32.4a 31.4b 33.7c 32.4a 0.23 AFIV 455a 393b 462a 455a 3.8 ANV 5.40a 6.37b 5.61c 5.51d 0.05 RESID 0.359a 0.342b 0.363a 0.361a 0.003 1 Values within a row without a common superscript differ (P<0.05). 2 Traits: FCR = feed conversion ratio (feed/gain); ADG = average daily gain (kg/day); LD = loin depth (mm); BF = backfat depth (mm); ADFI = average daily feed intake (kg/day); AOTD = average occupation time per day (s/day); AOTV = average occupation time per visit (s/visit); AFRV = average feeding rate per visit (g/min); AFIV = average feed intake per visit (g/visit); ANV = average number of visits; CADFI = residual daily feed intake (kg/day).

Previous studies have shown that differences in group size affect feeding behavior. Hyun and

Ellis (2002) found that, as group size increased, ANV and AOTD decreased and AFIV, AOTV, and AFRV increased. Other studies support these findings such as Nielsen et al. (1995) which found that, as group size increased, ANV and feeder AOTD decreased whereas AOTV, AFIV, and AFRV increased. Both studies found no significant effect of group size on ADG, FCR, or ADFI (Hyun and Ellis, 2002; Nielsen et al., 1995). The lack of an effect of group size on performance traits when group size does effect feeding behavior may explain some of the differences in results between crossbred and purebred pigs. Crossbred pigs had more pigs per pen than purebred pigs and, therefore, would be expected to have different feeding behavior from purebred pigs.

Previous studies have found differences between sexes for performance and feeding behavior traits. Boars have been shown to have higher ADG and lower FCR than gilts (de Haer and de Vries,

70

70

1993; Hyun et al., 1997). Hyun et al. (1997) found that barrows had higher ADG than gilts and FCR intermediate to but not different from boars and gilts. Hyun et al. (1997) also found that although barrows ate more meals per day than boars and gilts, there were no differences between sexes for other feeding behavior traits. This differs from the findings of de Haer and de Vries (1993) where gilts had more frequent visits to the feeder but consumed less feed per visit than boars. Labroue et al. (1994) found that barrows ate longer both by day and by visit and had higher ADFI than boars with no difference in the number of meals consumed per day. These studies show that the effect of sex on feeding behavior is still unclear. Therefore, the difference between crossbred and purebred pigs may also be influenced by sex.

A third possibility for differences between crossbred and purebred pigs is the effect of dam line on crossbred performance and behavior. Previous studies have shown that breed has an effect on feeding behavior. Labroue et al. (1994) found that, when penned together, French Landrace pigs had ANV, AFIV, and AOTV than Large White pigs. When penned separately, the only feeding behavior that differed significantly was the ANV with French Landrace pigs having lower ANV than Large White pigs (Labroue et al., 1994). De Haer and de Vries (1993) found that Dutch Landrace and Great Yorkshire pigs differed in both performance and feeding behavior. Great Yorkshire pigs had higher ADG and lean percentages, lower FCR, and less backfat than Dutch Landrace pigs. Great Yorkshire pigs also ate more frequently and faster than Dutch Landrace pigs with more ANV, lower AFIV, higher AFRV, and lower AOTD (de Haer and de Vries, 1993).

Hall et al. (1999) looked at the predicted responses to selection when including feeding behavior traits along with ADG, BF, and ADFI in a selection index. The three traits that Hall et al. (1999) looked at were AFIV, ANV, and AOTV because they had favorable correlations with performance traits and other feeding behavior traits are a function of those three traits, therefore adding no new information. Those three traits also happen to be the three traits that primarily compose PC1 in this study. Hall et al. (1999) concluded that the use of feeding behavior traits increased genetic gain potential for ADG, percent lean, FCR, and ADFI but accuracy of selection was decreased. Hall et al. (1999) also concluded that the most effective and robust index included ADG, BF, ADFI, and ANV.

LITERATURE CITED De Haer, L. C. M. and A. G. de Vries. 1993. Effects of genotype and sex on the feed intake pattern of

group housed growing pigs. Livest. Prod. Sci. 36:223-232. De Haer, L. C. M., P. Luiting, and H. L. M. Aarts. 1993. Relations among individual (residual) feed intake,

growth performance and feed intake pattern of growing pigs in group housing. Livest. Prod. Sci. 36:233-253.

Hall, A. D., W. G. Hill, P. R. Bampton, and A. J. Webb. 1999. Predicted responses to selection from indices incorporating feeding pattern traits of pigs using electronic feeders. Anim. Sci. 68:401-412.

Hyun, Y., M. Ellis, F. K. McKeith, and E. R. Wilson. 1997. Feed intake pattern of group-housed growing-finishing pigs monitored using a computerized feed intake recording system. J. Anim. Sci. 75:1443-1451.

Hyun, Y. and M. Ellis. 2001. Effect of group size and feeder type on growth performance and feeding patterns in growing pigs. J. Anim. Sci. 79:803-810.

Hyun, Y. and M. Ellis. 2002. Effect of group size and feeder type on growth performance and feeding patterns in finishing pigs. J. Anim. Sci. 80:568-574.

Labroue, F., R. Guéblez, P. Sellier, and M. C. Meunier-Salaün. 1994. Feeding behaviour of group-housed Large White and Landrace pigs in French central test stations. Livest. Prod. Sci. 40:303-312.

Nielsen, B. L., A. B. Lawrence, and C. T. Whittemore. 1995. Effect of group size on feeding behaviour, social behaviour, and performance of growing pigs using single-space feeders. Livest. Prod. Sci. 44:73-85.

Young, R. J. and A. B. Lawrence. 1994. Feeding behaviour of pigs in groups monitored by a computerized feeding system. Anim. Prod. 58:145-152.

71

71

Managing Selection and Diversity in a Breeding Program

Scott Newman, Valentin Kremer, and Brian Kinghorn Genus, plc and PIC North America

Introduction Livestock breeders juggle many issues when making breeding decisions. One approach to solving these problems is to follow sets of rules recommended by geneticists and other practitioners. For example,

• We might use a particular set of economic weightings in an index, or rank and select boars based on a specific trait EBV;

• To minimize inbreeding effects we might use no more than two boars out of any one sire, not mate full sibs, and(or) mate each boar to the same number of females;

• We might choose to cull sows after two mating years no matter what her level of performance might be. These rules have been derived from generalized concepts and theories, and are often not well integrated with each other. For example, theories and rules about selection, crossbreeding and inbreeding have been developed largely in isolation from each other, such that when we mix them in real applications we are likely to miss the best overall strategy. There can also be the added advantage in making decisions tactically, rather than following a pre-set strategy. The tactical approach makes use of knowledge of the full range of actual animals available for breeding at the time of decision making, as well as other factors such has the availability of farrowing houses, current costs of semen, current quarantine restrictions on animal migration, current or projected market prices, etc. Tactical implementation of the breeding program provides the opportunity to capitalize on prevailing opportunities – ones that would be often missed when adhering to a set of rules. In other words

“Let the design emerge as a consequence of the actions taken tactically” In this way, technologies are adopted only where appropriate and there is better balance between technical, logistical and cost issues. In genetic improvement there are an infinite range of actions, but in reality only two critical control points – animal selection and mate allocation. Together these constitute Mate Selection. Because the best animals to select depend on the pattern of mate allocation, and vice versa, these decisions can be made simultaneously as mate selection. The user decides what mating pairs and groups to make, and the resulting report is simply a mating list. When we specify the implementation of the breeding program using this mate selection approach, we automatically incorporate decisions on factors such as breeding objectives, selection pressure, crossbreeding, inbreeding avoidance, which animals to take semen and embryos from, migration of sires between herds, and how much to spend on seedstock purchase, transport etc. Moreover, we can also satisfy any logistical constraints we want to impose, such as quarantine restrictions on animal movements. Balancing Selection and Inbreeding The relationship between selection and inbreeding can best be observed in Figure 1. The x-axis is the predicted rate of inbreeding for progeny from hypothetical mating sets generated during the optimization process. The y-axis is index value. The upper portion of the “frontier” reflects a breeding program using fewer sires and making rapid genetic gain but also incurring higher rates of inbreeding. The lower point represents a breeding program using a larger number of sires (and lower inbreeding) but lower genetic gain. The optimum solution is somewhere between these two points. However, since selection is a dynamic process, we cannot expect that point on the frontier to stay constant.

72

72

Figure 1. Graphical representation of the relationship between predicted progeny inbreeding (x-axis) and predicted progeny index value (y-axis). Higher targets along the “genetic frontier” yields higher response and higher inbreeding. The current mating set is the “target” with a trail behind showing changes as the analysis progresses and as the user changes direction.

Implementing Mate Selection The mate selection approach to breeding is driven by specifying desired outcomes. An outline of the approach is shown in Figure 1. For each mating set tested, the component outcomes evaluated constitute the overall Mate Selection Index (MSI). Each component must be evaluated on the same scale, typically the scale of the breeding objective in units of, for example, dollars profit per breeding female per year. The MSI can be set to an arbitrarily low and uncompetitive value (e.g. minus 999999) for mating sets that break a constraint - for example mating sets that imply migration against a hard quarantine barrier, or greater use of liquid funds than a limit specified by the breeder or group.

Figure 2. An outline for implementation of a mate selection index. The set of matings shown is a hypothetical test mating set. The matings specified imply the need for collection of semen, etc., as shown. The mating set is evaluated for all components in the MSI. An efficient algorithm for finding the best mating set is required.

Progeny Inbreeding

Prog

eny

Inde

x

Progeny Inbreeding

Prog

eny

Inde

x

Progeny Inbreeding

Prog

eny

Inde

x

Progeny Inbreeding

Prog

eny

Inde

x

73

73

The MSI is a function of the selections and mate allocations chosen across all mating individuals in the population. The MSI is used to form a mating set for the specified number of matings in the breeding program. The MSI can include genetic gain, inbreeding, heterosis (if relevant) and various costs associated with the breeding program (Banks 2000; Kinghorn 2000). The objective function can be changed (variables added or deleted) to suit the desired outcomes of the breeder if the breeding objective changes or constraints arise. For example, a typical objective function is:

tCFMAxx

MGxMSI cos

42 2 −++′

+′

= χφλ

Definition of the components of the MSI can be found in Table 1. The first two components of the MSI constitute the optimal contributions for the mating set. The numerator relationship matrix for candidates is represented by A

and 2Axx′

is coancestry. Coancestry is a measure used to reflect the rate of inbreeding. As the numerator

relationship between two animals is twice the inbreeding predicted in their progeny, thus coancestry reflects the rate of inbreeding. To find the optimal mating set evolutionary algorithms that maximize the MSI are often used. An evolutionary algorithm is the general term to describe computer based solving systems which use computational models of evolutionary processes as key elements in their design and implementation. The type of evolutionary algorithm that is used for some applications is an evolution strategy called differential evolution. Differential evolution accepts real values and is able to quickly optimize the function for these real values over many generations (Price and Storn 1997). This means that by applying differential evolution an optimal mating set can be achieved when assessing all desired outcomes in the MSI. This computational method of maximizing the MSI by optimizing the evolutionary algorithm will continue for many “generations” until the optimal mating set is found. This is achieved by comparing and then recombining and randomly mutating each joined mating set to achieve a “better” set of matings. Finally, the breeder will select the most optimal mating set. This is achieved when the evolutionary algorithm (EA) has converged. Table 1. Component definitions of a sample mate selection index (MSI)

Item Definition x A vector of number of matings to be made for each candidate over both sexes G A vector of genetic merit containing selection index values for candidates M Total number of matings to be made – usually the number of breeding females λ Weighting factor (typically negative) to discourage low effective population sizes A The numerator relationship matrix for candidates φ Weighting on predicted progeny inbreeding F Predicted progeny mean inbreeding χ Weighting on predicted progeny mean crossbred value C Predicted progeny crossbreeding value cost Cost of the mating policy implied by x Mate Selection Applications Some mate selection applications do exist and are commercially available. Some of the more accessible applications are summarized below. 1. Total Genetic Resource Management (TGRM).

The ability to utilize mate selection on a large scale became a reality in 1997 through the use of an evolutionary algorithm that allowed the value of different matings made to be accounted for. The first industry-wide applications of TGRM occurred in 1997 through the LAMBPLAN system of genetic evaluation in Australia (Kinghorn et al., 2002). A commercial service commenced in 1998 and continues to be offered to the Australian sheep and beef cattle industries through accredited operators. A commercial company (X’Prime Pty. Ltd.) was

74

74

formed in 2003 as a way to further commercialize TGRM technology to other livestock species and countries. See www.xprime.com.au for further information.

2. GENCONT

GENCONT was developed by Theo Meuwissen and is an application providing breeder information on how many progeny each candidate animal should obtain in a breeding program. The application does not cover as many breeding issues as TGRM, but it is relatively simple to implement. See Meuwissen (2002) and more recently Hinrichs et al. (2006). The program runs on a PC can be obtained from the developer for a fee.

3. Evolutionary Algorithm for Mate Selection (EVA) EVA was developed by Peer Berg in Denmark (Berg et al., 2006) and is available from the developer. EVA handles genetic gain, relatedness of parents and progeny inbreeding. (See http://www.nordgen.org/ngh/download/genviten3-2003-engelsk.pdf).

4. ANI-MATE ANI-MATE is a service provided by Abacus Biotech, a consulting company based in New Zealand. The program handles inbreeding avoidance (mate allocation) as well as management of long-term inbreeding by aiming to maximize genetic progress without increasing overall relatedness beyond given limits. See www.abacusbio.co.nz/products.html#animate.

Further Comments The basic information required to implement mate selection is normally collected in most genetic improvement programs, including sex of the animal (to separate male and female candidates); trait EBV and(or) index value (calculated from a BLUP run followed by application of a set of economic weights); and the candidate status (the maximum number of matings that can be made by the animal, which is usually one for females unless MOET is employed). This status defines a limit and does not mean that the animal will be automatically used for that number of matings. Other information that might be used, if available, includes marker genotype, or even breed genotype if crossbreeding effects are to be accommodated. Mate selection applications have been implemented in a number of livestock species – meat sheep and beef cattle in Australia; dairy cattle in New Zealand; pigs in the US, Australia and Europe; and poultry in Europe. The vehicle for delivery has been either the breed society or national genetic evaluation (as in the case for sheep and cattle in Australia) or through agreements with private service providers. Mate selection provides a very powerful framework for integrating outputs from a range of scientific tools (e.g., EBV, indexes, marker genotypes) with the knowledge, wisdom, and attitudes of practicing animal breeders. References Berg, P., J. Nielsen, and M.K. Sørensen. 2006. EVA: Realized and predicted optimal genetic contributions. Proc. 8th World Congress on Genetics Applied to Livestock Production [CD-ROM communication 27-09]. Banks, R. 2000. Genetic improvement in the animal breeding industries. Chapter 21 in “Animal Breeding – Use of New Technologies”, Kinghorn, B.P., Van der Werf, J.H.J. and Ryan, M. (Eds.). The Post Graduate Foundation in Veterinarian Science of the University of Sydney. ISBN 0 646 38713 8. Hinrichs, D. M. Wetten and T. H. E. Meuwissen. 2006. An algorithm to compute optimal genetic contributions in selection programs with large numbers of candidates. J. Anim. Sci. 84:3212–3218. Kinghorn, B. P. 2000. The tactical approach to implementing breeding programs. Chapter 22 in “Animal Breeding – Use of New Technologies”, Kinghorn, B.P., Van der Werf, J.H.J. and Ryan, M. (Eds.). The Post Graduate Foundation in Veterinarian Science of the University of Sydney. ISBN 0 646 38713 8. Kinghorn, B.P.; S.A. Meszaros and R.D. Vagg. 2002. Dynamic tactical decision systems for animal breeding. Proc. 7th World Congress on Genetics Applied to Livestock Production. [CD-ROM communication 23-07]

75

75

Meuwissen, T.H.E. 2002. GENCONT: An operational tool for controlling inbreeding in selection and conservation schemes. Proc. 7th World Congress on Genetics Applied to Livestock Production. [CD-ROM communication 28-20] Price, K. and Storn, R. 1997. Differential evolution. Dr. Dobb’s Journal, 18-24.

76

76

77

77

National Pork Board Research and Committee Update Mark Boggess, Ph.D

Director, Animal Science National Pork Board

Introduction This Update describes the National Pork Board 2007 Strategic Operating Plan and Budget for Science and Technology. All programming issues are organized by industry Critical Issue (CI) and Desired Outcome as put forward by the National Pork Board membership, with input from the working committees at the National Pork Board. Each tactic is assigned to a CI across programming disciplines. This approach encourages cooperation and collaboration across committees and departments at the NPB with a focus on proactive programming and problem solving for the pork industry. Science and Technology tactics for 2007 are described for each of committees in the 2007 Critical Issue framework. The 2007 Critical Issues for the National Pork Board are: Critical Issue # 1 - Positively impacting customer’s and consumer’s purchases of pork. Critical Issue # 2 - The trust and image of the industry and its products. Critical Issue # 3 - The development of human capital. Critical Issue # 4 - The profitability and competitive advantage for US pork. Critical Issue # 5 - The safeguard and expansion of international markets. The Science and Technology Committees at the national Pork Board include:

Animal Science Committee – Mark Boggess, Ph.D. Environment Committee – Allan Stokes Pork Safety Committee – Steve Larsen, Ph.D., Liz Wagstrom, DVM. Swine Welfare – Sherrie Niekamp Swine Health Committee – Pamela Zaabel, DVM., Patrick Webb, DVM. Vice President of Science and Technology – Paul Sundberg, Ph.D., DVM.

Listed below are the 2007 CI and the Desired Outcomes for each Critical Issues. Specific tactics relating directly to programming initiatives for Science and Technology are also listed. The complete Plan of Work for 2007, with all tactics and budget amounts for each CI, DO, and tactic is available through the National Pork Board (NPB). CI # 1 - Positively impacting customer’s and consumer’s purchases of pork. Desired Outcome # 1 - A plan aligning product attributes with specific customer and consumer expectations is in

place.

Competitive research will be conducted on priorities established by the Pork Safety Committee. Pork Safety Committee

Science-based information on pork safety throughout the chain will be provided through competitive research based on the Pork Safety Committee's Priorities. This will include research on the epidemiology of Salmonella throughout the chain, research on potential emerging food safety concerns, development of diagnostic tests and others.

Targeted research will be conducted on priorities established by the Pork Safety Committee. Pork Safety Committee

Science-based information on pork safety throughout the chain will be provided through targeted research in areas identified as emerging issues and based on the Pork Safety Committee's oversight. This will include research on new interventions throughout the chain, research on emerging food safety concerns, production system changes effects on pork safety and others.

78

78

Pork Safety and Quality research will be disseminated to stakeholders to support industry practices. Pork Safety Committee

Research increases in value as it is disseminated and used. Producers, packers, regulators and others will be provided results of the funded research. A Pork Quality and Safety Summit will be held. We will collaborate with other meat industry groups in research and programming leading to additional opportunities to be heard as one voice during regulatory and stakeholder discussions. One task will be to create a method for a unified response to misinformation and activist challenges.

Consumer Preference Study. Animal Science Committee Positively impact consumer demand and repeat customer purchases through completion of year 2 of the Pork Consumer Taste & Preference Study to determine the effects of pork attributes and end point temperature on consumer preference for fresh pork in the marketplace. This study is being conducted in cooperation with Ohio State University, Texas A&M University and Iowa State University.

Desired Outcome # 2 - A positive shift in consumers’ and customers’ attitudes toward pork transpired. Information on Take Care – Use Antibiotics Responsibly will be delivered to customers. Pork Safety Committee

The Take Care program will be communicated to a variety of audiences including customers, healthcare providers, consumer groups and government agencies to position the industry as proactively addressing consumer concerns and consumer advocacy misinformation about antibiotic use. This information will provide customers a credible position on antibiotic use in the pork industry.

Address Customer Environmental Issues. Environment Committee A strategy for addressing customer concerns relative to the pork industries environmental performance that might impact on their decisions for marketing and showcasing pork products is developed and put into action. Will utilize PPIC as an information gathering tool.

Desired Outcome # 3 - Pork producers understand the quality factors that drive pork purchases.

Increase awareness of broken needles and producer responsibilities for appropriate interventions. Pork Safety Committee

Needles and other physical hazards in pigs and pork products are a continuing concern and impact consumer confidence in pork. Messages and communication strategies will be developed and delivered to deal with producer’s responsibilities to prevent physical hazards.

Desired Outcome # 4 - A profile of the moral and ethical concerns of consumer’s regarding meat consumption and modern agriculture is in place as is a process for responding to emerging opportunities and challenges.

Societal concerns about food safety, antibiotic use or public health effects of pork production. Pork Safety Committee

Societal concerns in pork safety, antibiotic use, or public health effects of pork production will be identified and managed through technical working group meetings, Committee meetings, staff travel, and interactions with stakeholders throughout the chain. Science-based tactics to address these concerns will be identified and implemented, and the impact of tactics to meet identified concerns assessed.

Critical Issue # 2 - The trust and image of the industry and its products. Desired Outcome # 1 - A definition of socially responsible pork production is in place, a process for repositioning

the image of the industry underway, and the tools and information to help the industry meet the definition are successful.

Competitive research will be conducted on antibiotic use and other public health topics. Pork Safety Committee

Science based information on the public health impacts of pork production (such as antibiotic use, community health effects of pork production, endocrine disruptors, and others) will be generated through

79

79

competitive calls for research, including exploration of potential interventions and collaborations with other research entities.

Address the potential/perceived public health issues with pork production. Pork Safety Committee Interactions with regulatory, environmental and health agencies addressing potential/perceived public health issues with pork production will cause them to view the Pork Checkoff as a source of technical expertise and the pork industry as stakeholders. Information will be developed, delivered to a variety of audiences, and made available to producers, including topics such as respiratory health, endocrine disruptors, zoonotic disease such as influenza, etc. Research priorities will be identified.

Research projects targeting specific public health or antibiotic use issues. Pork Safety Committee Targeted research priorities will be identified by forums such as emerging issues focus groups. Science based information on the issue of antibiotic resistance, health effects of pork production, zoonotic disease or other public health topics will be developed through funding of these targeted proposals. This information will assess risk and/or develop practical interventions if needed. It is important to be able to react to emerging issues through research to address these specific targets.

Information regarding animal feed safety as related to public health will be developed and delivered. Pork Safety Committee

With FDA and the state regulators developing new programs to address the safety of non-medicated feed, comprehensive programming and development, including educational materials and research information, are needed to provide producers information to be able to comply with the new regulatory programs. Technical input will be provided to the appropriate audiences to help inform them on the public health aspect of feed safety.

Desired Outcome # 2 - Key segments of the pork chain understand and support PQA Plus.

Desired Outcome # 3 - Positive news on pork is utilized by “influencer” segments, and communicated to both

consumer and producer audiences. Desired Outcome # 4 - Key influencer segments are effectively communicating better preparation methods of pork

to consumers. Desired Outcome # 5 - Progress with the government was realized leading to a reduction in the endpoint cooking

temperature of pork. Risks from lower cooking temperatures will be assessed, and if needed interventions identified. Pork Safety Committee

Messages and risk communication strategies will be developed to deal with lowering the end point cooking temperatures of pork and if needed, interventions will be identified. Information will be developed to answer the government’s questions to lower cooking temperatures also.

Desired Outcome # 6 - A plan to address consumer confidence in product claims/definitions is in place. Critical Issue # 3 - The development of human capital. Desired Outcome # 1 - US pork production labor challenges have been assessed and a plan to address completed.

Desired Outcome # 2 - Tools that support education, recruiting, training and the retention of quality personnel exist

and are being utilized. Development of research, labor and leadership resources in the pork industry. Animal Science Committee

Develop an industry based working group or “Blue Ribbon Panel” to evaluate opportunities to expand the development of the US Pork Center of Excellence model to identify resources and collaborative opportunities for research and education with university and industry entities as well as other pork

80

80

commodity stakeholders. This effort will identify priorities for future resource development to ensure the competitiveness of the US pork industry.

Sponsorship of industry programs to promote youth development for future leadership roles. Animal Science Committee

Support industry activities that promote youth and leadership development including the American Meat Science Association and the American Society of Animal Science including collegiate meat judging programs, Reciprocal Meats Conference, PORK 101 & PORK 105 and related programs. These programs are unique in their promotion of the pork industry and vital to the development of future leaders in the pork industry as well as providing continuing education for all stakeholders in the pork industry.

Desired Outcome # 4 - Future leaders of the pork industry have been developed and are supported in ways that

create mutual opportunities.

Critical Issue # 4 - The profitability and competitive advantage for US pork.

Desired Outcome # 1 - Science based pork industry solutions for health, production and food safety were advanced.

Animal welfare targeted research. Animal Welfare Committee

Provide producers with science based animal welfare information through targeted research addressing topics to be defined by the Animal Welfare Committee.

Animal welfare competitive research. Animal Welfare Committee Provide producers with science based animal welfare information through competitive research addressing topics to be defined and prioritized by the Animal Welfare Committee.

Targeted research funding provided for high priority research areas addressing swine health. Swine Health Committee

Healthy animals are the foundation for producer opportunity for profit and competitive sustainability. Swine health research programs address high priority health concerns. Research priorities are developed by the Swine Health Committee after additional input from veterinarians and other animal health experts. Projects are funded according to the research priorities of the Swine Health Committee.

The PRRS Initiative gives a research framework to researchers, producers, veterinarians, & industry. Swine Health Committee

PRRS costs producers $560 million each year. This unique pathogen has characteristics that challenge the traditional disease control strategies we have used successfully for many other diseases. While these tools can lessen the impact, they have not been able to prevent the occurrence of new outbreaks. A continued concerted and coordinated research effort needs to be applied to the study of the virus to develop tools and management strategies that will reduce the economic impact of PRRS.

Research priorities identified during the PCVAD Workshop will address PCVAD through research. Swine Health Committee

PCVAD is an emerging disease affecting US swine producers. A PCVAD Strategic Planning Workshop was organized by Checkoff to facilitate a research discussion among producers, veterinarians, researchers, and allied industries. A coordinated research plan was developed containing PCVAD research priorities with a goal of mitigating the economic effect of this disease. A competitive research call will address the priorities outlined in the strategic plan.

Swine Health Committee and Advisory Meetings. Swine Health Committee Throughout 2007, there will be numerous established state and federal meetings covering swine health issues. In many instances, ad hoc meetings are announced at which swine industry representation is critical. Meeting agendas address swine health issues and affect implementation of programs that will directly or indirectly affect swine health and the opportunity for producer profit. To ensure producer input is provided, producers and/or their representatives will attend.

81

81

Address and respond to production issues regarding pork safety and human health. Pork Safety Committee

The Pork Safety Committee and their advisory groups will meet to determine appropriate programming and responses to pork safety and human health concerns. The staff, committee and their advisors will engage in scientific forums, and other venues to gather information and respond to misinformation regarding food safety and public health.

Research programs to determine the ability of genetic resistance to mitigate PRRS and PCV2. Animal Science Committee

Develop a targeted research effort to identify genetic tools to mitigate the economic impact of PRRS and PCV2. Work cooperatively with allied industry, the USDA PRRS Cap II program and the NPB PRRS Initiative to identify additional sources of funding. Evaluate the potential to lessen economic impact, enhance the efficiency of vaccine application and enhance initiatives to eliminate pathogens from regional populations. This research will be enhanced by the swine genome sequencing project.

Research to mitigate soy allergens in nursery rations. Animal Science Committee Targeted research to mitigate soy allergens in nursery rations to enhance the industry’s ability to maintain competitiveness globally and with competing protein sources in cooperation with the United Soybean Board, the USDA and Purdue University. This research will improve the efficiency of nursery rations by lessening the allergenicity of soybean meal fed to nursery pigs.

Address Emerging Environmental Issues through Committee Work. Environment Committee A process for tracking and analyzing emerging environmental issues relative to their potential impacts on pork producers is in place.

Educational programs for sow longevity concerns. Animal Science Committee

Continue development of a Sow Productivity Management Guide to assist producers directly with issues associated with sow longevity and mortality. Combine this effort with the development of a Discovery Conference if appropriate for the industry. These programs will provide answers for producers relating to sow productivity and longevity and assist them with measurable improvement in sow productivity and longevity.

Animal Science Committee Meetings to Identify and Act on Emerging Issues. Funding is proposed to facilitate the appropriate NPB Animal Science response to emerging issues in the pork industry.

PCV2 Clearing House. Swine Health Committee Develop and populate a clearing house for production and research information addressing porcine circovirus type 2 (PCV2). Information will be compiled then deployed on a web site segmented into two areas, one for producers and one for researchers and veterinarians. Information provided will include: current research, completed research, fact sheets, articles, and links to other resources.

Competitive Environmental Research. Swine Health Committee Competitive research efforts according to Environmental Committee priority are conducted into improved, more effective and more economically viable environmental technologies and management practices and their economic effects.

Desired Outcome # 2 - Advancements were made in the pork industry’s production practices which measurably improved its operating capabilities.

Identify, monitor, and respond to national and international animal welfare issues. Swine Welfare Committee

Through meetings and discussions with animal welfare experts, producers, or other stakeholders, identify, monitor, and respond to national and international animal welfare issues that may affect the ability of

82

82

producers to make production decisions and to prevent animal welfare from becoming a limiting factor in international trade.

Resources are developed that promote measures to increase and protect herd health. Swine Health Committee

Biosecurity practices are important to swine producers aiding in the prevention of disease introduction or spread in swine production facilities. Producers would benefit from the development of a tool to evaluate the risk of infectious disease entry and spread into their operation. In addition, producers would benefit from information that specifically addresses basic epidemiology and biosecurity for individual diseases.

Targeted Environmental Research. Environment Committee Targeted research efforts according to Environmental Committee priorities are conducted into specific environmental issues to develop information on specific issues including: quantifying the energy offset and value of manure savings, research manure application on grasses for biofuels, research methods for stabilizing and capturing nitrogen and ammonia and alternative uses/products from manure and continued technical support for the National Air Emissions Study.

Desired Outcome # 3 - A plan that evaluates options for sow housing systems based on the criteria of animal well being, satisfying customer expectations, and meeting the test of workable and affordable is in place.

Management tools for sow housing systems. Animal Science Committee

Develop tools to help producers better understand optimal management strategies for a variety of types of sow housing systems.

Science-based evaluation of sow housing systems to address sow longevity. Swine Welfare Committee, Animal Science Committee Further evaluation of sow housing systems will demonstrate the industry’s commitment to science-based welfare solutions that are acceptable to the components of the pork chain. Through competitive research, conduct a science-based evaluation of sow housing systems, the impact of housing on sow longevity and the factors affecting sow longevity.

Desired Outcome # 4 - US pork producers have begun implementation of PQA Plus.

PQA Plus. Swine Welfare Committee, Producer Education Department

The PQA Plus program will be available for producers to attain individual certification and site certification. The program will include work preparing the marketplace, the launch of the program, and marketing of the program following it’s' rollout.

Youth PQA. Swine Welfare Committee, Producer Education Department Youth Pork Quality Assurance is an extension of the PQA Plus program that is focused at youth ages 8-18 years of age. This program will deliver educational Quality Assurance materials to youth that are involved in the pork industry. States' Quality Assurance programs will meet minimum national standards to certify youth in a Youth PQA program. The largest effort will be to develop and update materials that correspond with PQA Plus content. 25,000 youth will participate in Youth PQA in 2007.

Desired Outcome # 5 - Pork producers understand the strategies and tools available to them for mitigating current and emerging risks to profitability.

Crisis communications planning and management. Swine Health Committee

To assure that the National Pork Board and state organizations are prepared to manage any event that could cause harm to the pork industry. This includes developing specific staff assignments and the continuing development of a crisis communications plan. This tactic also incorporates crisis planning with other government and commodity organizations.

Desired Outcome # 6 - In the face of competition for feed grains, a strategy for maintaining competitive feed costs

is in place for US pork producers.

83

83

Educational programs for producers using distillers grains from ethanol production. Animal Science Committee

Maintain the industry’s ability to maintain competitiveness globally and with competing protein sources by addressing economic and production issues created by mandated ethanol production and increasing fuel costs via pork industry specific educational programs for pork producers. This program will assist producers with the evaluation and application of distillers grains for swine rations. Will use web based tools for information delivery.

Swine Energy Systems Research. Animal Science Committee Targeted research to evaluate energy systems for grow-finish ration in the US and Canada in cooperation with the United Soybean Board, the Prairie Swine Center, the University of Missouri, the University of Illinois, Purdue University and the USDA. This research will provide the industry with much needed information regarding energy utilization for ration development and will enhance the accuracy and efficiency of swine rations in the US utilizing corn and soy.

Critical Issue # 5 - The safeguard and expansion of international markets. Desired Outcome # 1 - An assessment of international customer needs for food safety, pork $1 quality and product

development has been completed, and a plan to address the findings prepared and distributed to appropriate industry partners for action.

Identification and response to pork safety and public health needs of trading partners. Pork Safety Committee

Pork safety and antimicrobial use practices have the ability to serve as potential trade barriers. In order to protect our current exports and maximize new market export potential pork safety and public health concerns of international trading partners will be identified and responded to as needed. We will need to develop strategies to help producers meet those specifications requested by importing countries.

Desired Outcome # 2 - US pork exports increased 10% in volume in 2007.

Desired Outcome # 3 - US pork producers understand the expectations and requirements of their international customers and their role in safeguarding the markets.

Desired Outcome # 4 - An assessment and evaluation of risk management options that will assure business

continuity in the event of US export interruption is complete. Desired Outcome # 5 - A national animal health surveillance system for US pork is in place.

Develop and encourage state-specific Swine Health Advisory Committees. Swine Health Committees

SHACs will provide early warning swine health surveillance and communicate local issues to the national level. SHACs will be modeled after PRV Advisory Committees and consist of producers, veterinarians, and industry representatives. The outcomes generated from SHAC inputs will be communicated to SHAC’s for review. SHACs will use outcomes to educate all segments of the state swine industry regarding swine health activities. SHACs were recognized in the Swine Futures Projects as a critical need.

Producers’ input will be provided to a comprehensive swine health surveillance system. Swine Health Committee

Swine disease surveillance is critical for maintaining and expanding markets. A comprehensive swine disease surveillance program will need to have a structure for producer input in order to address industry needs. SHACs and an organized system of expert advisory groups will provide input through the Swine Health Committee to the USDA surveillance unit.

Leadership for exchange of information between swine industry and the National Surveillance Unit. Swine Health Committee

84

84

In order to identify the amount of funding necessary for a comprehensive and effective swine disease surveillance program, the industry has to provide surveillance objectives to the National Surveillance Unit (NSU). This information will be used in a pathway analysis that will identify the funding necessary to meet surveillance goals. The amount can then be communicated to industry stakeholders to support national surveillance programs.

The Swine ID Plan is essential to supporting the future of swine health. Swine Health Committee The Swine ID Plan is essential for supporting rapid containment and eradication of highly contagious swine diseases, and will enable appropriate continued movement and marketing during disease outbreaks. In addition, the Swine ID Plan can support targeted surveillance for highly contagious diseases and other emerging diseases or syndromes. Producer and industry acceptance and participation is a vital component for plan implementation and its success supporting swine health.

Zoonotic and food safety issues are included in surveillance systems. Swine Health Committee Checkoff will work with USDA, FDA, and others to address zoonotic and food safety issues are included in appropriate surveillance systems. Surveillance on Trichinella will be imperative to open new markets without costly carcass testing. If Avian Influenza becomes a global issue, surveillance will potentially protect U.S. swine markets.

Desired Outcome # 6 - The US pork industry is a key contributor in steering decisions at international standard setting events and proceedings.

Monitor and provide technical input to Codex food safety activities with potential to impact pork. Pork Safety Committee

Codex Alimentarius sets the standards by which sanitary and phytosanitary barriers to trade are established. The Food Industry Codex Coalition is an organization of commodities and others that give input to the US delegation to Codex Committees. It will be important to be involved in Codex discussions to help assure that food safety does not become a barrier to exporting US pork.

85

85

National Swine Improvement Federation 2006 Committees

Audit Committee

Chair, Everett Forkner

Mark Boggess

Awards Committee

Chair, Glenn Conatser

Tom Baas

Sam Buttram

Doug Stewart

Doug Newcom

Certification Committee

Chair, Steve Moeller

Tom Baas

Tom Rathje

Jay Lampe

Dallas McDermott

Fact Sheet Committee

Chair, Ron Bates

Archie Clutter

Dan Hodges

Ken Stalder

Joe Cassady

Guidelines Committee

Chair, Todd See

Tom Long

John Mabry

Tom Rathje

Jack Dekkers

Membership Committee

Chair, Ron Bates

Rick Pfortmiller

Dale Miller

Fields Gunsett

Program Committee

Chair, Doug Newcom

Scott Price

Dale Miller

Scott Newman

Max Waldo

Ken Stalder

Mark Boggess

Technical Committee

Chair, Rodger Johnson

Daryl Kuhlers

86

86

National Swine Improvement Federation Officers and Directors, 2005 - 2006

Officers President Joe Cassady (2006 – 1st term starts) North Carolina State University North Carolina State University 232B Box 762 Raleigh, NC 27695 919-513-0262 919-513-7780 joe_cassady@ncsu.edu Vice President Doug Newcom (2006 – 1st term) Genetic Improvement Services PO Box 9 6980 Harper House Rd. Newton Grove, NC 28366-0009 Phone 910-594-2353 910-594-0238 Fax

dnewcom@gis-swine.com Editor Ronald O. Bates (2006 – continuing) Michigan State University 1205 Anthony Hall East Lansing, MI 48824 517-432-1387 517-353-1699 FAX batesr@pilot.msu.edu Secretary-Treasurer Steve Moeller (2006 - continuing) Ohio State University 2029 Fyffe Rd. Columbus, OH 43210 614-688-3686 614-292-3513 FAX moeller.29@osu.edu

Ex Officio Members USDA-CSREES Muquarrab A Qureshi (2006 - continuing) Ntnl. Pgm. Ldr., Anim. Gen. USDA-CSREES Room 3441 Waterfront Centre 1400 Independence Ave., SW Washington, DC 20250-2220 202-401-4895 202-401-1602 FAX mqureshi@csrees.usda.gov NC-1004 Research Group Jack Dekkers (2006 - continuing) Iowa State University 225 Kildee Hall Ames, IA 50011 (515) 294_7509 (515) 294_9150 FAX jdekkers@iastate.edu

NSRP-8 Research Group Max Rothschild (2006 - continuing) 225 Kildee Hall Iowa State University Ames, IA 50011 515-294-6202 515-294-2401 FAX mfrothsc@iastate.edu Land Grant University Maynard Hogberg (2006 – continuing) 1221 Kildee Hall Iowa State University Ames, IA 50011-3150 515-294-2160 515-294-6994 FAX hogberg@iastate.edu

Associate Member Organizations Dale Miller (2006 – continuing) National Hog Farmer 7900 International Dr. #300 Minneapolis, MN 55425 612-851-4661 612-851-4601 FAX dpmiller@primediabusiness.com

Nat’l Pork Board Executive Board Mark Boggess (2006 – continuing) Nat’l Pork Board PO Box 10383 Des Moines, IA 50306 515-223-2600 515-223-2646 FAX Mark.Boggess@porkboard.org

87

87

Directors at Large Scott Price ISCF Genetics 229 Hwy 111 South Goldsboro, NC 27534 919-778-6066 ext 101 iscfscottprice@bellsouth.net Joe Cassady (1st term, 2nd year) North Carolina State University 232B Box 762 Raleigh, NC 27695 919-513-0262 919-513-7780 joe_cassady@ncsu.edu Ken Stalder (1st term, 2nd year) Iowa State University 109 Kildee Hall Ames, IA 50011-3150 (515) 294-4683 (515) 294-5698 FAX stalder@iastate.edu

Tom Baas (2006 - 2nd term, 2nd year) Iowa State University 109 Kildee Hall Ames, IA 50011-3150 (515) 294-6728 (515) 294-5698 FAX tjbaas@iastate.edu Archie Clutter (2006 – 1st term, 2nd year) Monsanto Choice Genetics 800 N. Lindberg B2NA St. Louis, MO 63167 archie.c.clutter@monsanto.com

Swine Breed Associations Darrell Anderson (2006 - 1st term, 1st year) National Swine Registry PO Box 2417 1769 US Hwy 52 West West Lafayette, IN 47996 (765) 463-3594 (765) 497-2959 FAX darrell@nationalswine.com Doug Stewart (2006 1st term, 1st year) Stewart’s Duroc Farm, Inc. 1750 212th Street Waverly, IA 50677 (319) 352-1709 stewarts@wl-p.net

Shannon Witzig (2006 – 1st term, 2nd year) 28501 N. 2025 E. Rd. Gridley, IL 61744 309-747-2423 Max Waldo (2006 – 1st term, 2nd year) Waldo Farms DeWitt, NE 68341 402-683-5225 402-683-6605 FAX max@waldofarms.com

National Pork Board Everett Forkner (2006 – 1st term, 2nd year) TruLine Genetics Rt 1 Box 19 Richards, MO 64778 417-484-3306 417-484-3317 FAX

eforktlg@klmtel.net Dan Hodges (2006 - 1st term, 2nd year) 6223 S Road Julian, NE 68378-8400 402-242-2251 402-242-2191 FAX mh52443@alltel.net

Central Swine Test Stations/Orgainzations Dallas McDermott (2006 – 1st term, 2nd year) Mac Scan 1314 Baldwin St, Harlan, IA 51537 712-755-2190, 712-755-5332 FAX, macscan@harlannet.com

88

88

Artificial Insemination Companies Philip Dorn (2006 – 1st term, 2nd year) Birchwood Genetics 465 Stephens Rd. West Manchester, OH 45382 937-678-9313 937-678-9323 FAX pwdorn@juno.com

Harold Hodson (2006 – 2nd term, 2nd year) Swine Genetics Intern’l 30805 595th Cambridge, IA 50046 515-383-4386 515-383-2257 FAX swinegenetics@worldnet.att.net

Breeding Companies Scott Newman (2006 – 1st term, 1st year) PIC PO Box 348 Franklin, KY 42135 scott.newman@pic.com Fields Gunsett (2006 - 1st term, 2nd year) Newsham Genetics 5058 Grand Ridge Drive, Suite 200 West Des Moines, IA 50265 515-225-9420 515 225-9560 FAX Fields_Gunsett@newsham.com

Dan Hamilton (2006 - 1st term, 2nd year) Genetiporc Inc. 23886 N. County Hwy 38 Marietta, IL 61459 daniel.hamilton@att.net Doug Newcom (2006 – 1st term, 2nd year) Genetic Improvement Services PO Box 9 6980 Harper House Rd. Newton Grove, NC 28366-0009 Phone 910-594-2353 910-594-0238 Fax dnewcom@gis-swine.com

89

89

2006 NSIF Membership

Babcock Genetics Birchwood Genetics California State University -Fresno Chico State University Classic Medical Supply Compart's Boar Store Designed Genetics Farmland Foods Genetiporc GIS of North Carolina Global Pig Farms Heritage Swine Genetics HermitageNGT Hormel Foods Hypor, Inc Ivey Spring Creek Farms Micro Beef Technologies National Pork Board National Swine Registry Nebraska SPF Newsham Hybrids North Carolina State University Ohio Pork Improvement Association Ontario Swine Improvement PIC Prism Business Media S&S Programming

Swine Genetics International